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https://gitee.com/mirrors_PX4/PX4-Autopilot.git
synced 2026-11-08 15:41:37 +08:00
WIP: ekf2 optional double precision floating point
This commit is contained in:
Daniel Agar
@@ -15,6 +15,7 @@ CONFIG_MODULES_DATAMAN=y
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CONFIG_MODULES_DIFFERENTIAL_DRIVE=y
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CONFIG_MODULES_EKF2=y
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CONFIG_EKF2_VERBOSE_STATUS=y
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CONFIG_EKF2_DOUBLE_PRECISION_FLOAT=y
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CONFIG_EKF2_AUX_GLOBAL_POSITION=y
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CONFIG_MODULES_EVENTS=y
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CONFIG_MODULES_FLIGHT_MODE_MANAGER=y
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@@ -71,6 +71,9 @@ T sq(T val)
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return val * val;
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}
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// constexpr float sq(double val) { return static_cast<float>(val * val); }
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// constexpr double sq(float val) { return static_cast<double>(val * val); }
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/*
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* So called exponential curve function implementation.
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* It is essentially a linear combination between a linear and a cubic function.
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@@ -68,12 +68,20 @@ constexpr _Tp max(_Tp a, _Tp b)
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return (a > b) ? a : b;
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}
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constexpr double max(float a, double b) { return ((double)a > b) ? (double)a : b; }
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constexpr double max(double a, float b) { return (a > (double)b) ? a : (double)b; }
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template<typename _Tp>
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constexpr _Tp max(_Tp a, _Tp b, _Tp c)
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{
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return max(max(a, b), c);
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}
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constexpr double constrain(double val, double min_val, double max_val)
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{
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return (val < min_val) ? min_val : ((val > max_val) ? max_val : val);
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}
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template<typename _Tp>
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constexpr _Tp constrain(_Tp val, _Tp min_val, _Tp max_val)
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{
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@@ -157,6 +157,19 @@ public:
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dcm = Quaternion<Type>(aa);
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}
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template<typename S>
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Dcm(const Dcm<S> &aa)
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{
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Dcm &dcm = *this;
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for (int i = 0; i < 3; i++) {
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for (int j = 0; j < 3; j++) {
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dcm(i, j) = static_cast<Type>(aa(i, j));
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}
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}
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}
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Vector3<Type> vee() const // inverse to Vector.hat() operation
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{
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const Dcm &A(*this);
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@@ -55,6 +55,18 @@ public:
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}
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}
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template<typename S>
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Matrix(const Matrix<S, M, N> &aa)
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{
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Matrix &m = *this;
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for (size_t i = 0; i < M; i++) {
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for (size_t j = 0; j < N; j++) {
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m(i, j) = static_cast<Type>(aa(i, j));
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}
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}
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}
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template<size_t P, size_t Q>
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Matrix(const Slice<Type, M, N, P, Q> &in_slice)
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{
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@@ -115,7 +127,8 @@ public:
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return (*this);
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}
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void copyTo(Type dst[M * N]) const
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template<typename FloatType>
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void copyTo(FloatType dst[M * N]) const
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{
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const Matrix<Type, M, N> &self = *this;
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@@ -753,8 +766,8 @@ Type max(const Type x, const Type y)
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return (x > y) ? x : y;
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}
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template<typename Type>
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Type constrain(const Type x, const Type lower_bound, const Type upper_bound)
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template<typename Type1, typename Type2>
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Type1 constrain(const Type1 x, const Type2 lower_bound, const Type2 upper_bound)
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{
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if (lower_bound > upper_bound) {
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return NAN;
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@@ -87,6 +87,16 @@ public:
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{
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}
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template<typename S>
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Quaternion(const Quaternion<S> &other)
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{
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Quaternion &v(*this);
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v(0) = static_cast<Type>(other(0));
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v(1) = static_cast<Type>(other(1));
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v(2) = static_cast<Type>(other(2));
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v(3) = static_cast<Type>(other(3));
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}
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/**
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* Constructor from dcm
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*
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@@ -538,6 +548,20 @@ public:
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R_z(2) = a * a - b * b - c * c + d * d;
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return R_z;
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}
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operator Quaternion<double>() const
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{
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return Quaternion<double>((double)(*this)(0), (double)(*this)(1), (double)(*this)(2), (double)(*this)(3));
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}
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operator Quaternion<float>() const
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{
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return Quaternion<float>((float)(*this)(0), (float)(*this)(1), (float)(*this)(2), (float)(*this)(3));
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}
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};
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using Quatf = Quaternion<float>;
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@@ -293,6 +293,18 @@ public:
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return res;
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}
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// Vector < float, P < Q ? P : Q > diag() const
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// {
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// const SliceT<MatrixT, Type, P, Q, M, N> &self = *this;
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// Vector < float, P < Q ? P : Q > res;
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// for (size_t j = 0; j < (P < Q ? P : Q); j++) {
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// res(j) = static_cast<float>(self(j, j));
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// }
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// return res;
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// }
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Type norm_squared() const
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{
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const SliceT<MatrixT, Type, P, Q, M, N> &self = *this;
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@@ -64,6 +64,20 @@ public:
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return {x0, y0, this};
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}
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// operator SquareMatrix<double>() const
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// {
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// return SquareMatrix<double>((double)(*this)(0), (double)(*this)(1), (double)(*this)(2), (double)(*this)(3));
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// }
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// operator SquareMatrix<float>() const
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// {
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// return SquareMatrix<float>((float)(*this)(0), (float)(*this)(1), (float)(*this)(2), (float)(*this)(3));
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// }
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// inverse alias
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inline SquareMatrix<Type, M> I() const
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{
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@@ -31,6 +31,16 @@ public:
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{
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}
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template<typename S>
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Vector(const Vector<S, M> &other)
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{
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Vector &v(*this);
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for (size_t i = 0; i < M; i++) {
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v(i) = static_cast<Type>(other(i));
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}
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}
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template<size_t P, size_t Q>
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Vector(const Slice<Type, M, 1, P, Q> &slice_in) :
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Matrix<Type, M, 1>(slice_in)
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@@ -171,6 +181,7 @@ public:
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{
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return M;
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}
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};
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template<typename OStream, typename Type, size_t M>
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@@ -40,6 +40,14 @@ public:
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v(1) = y;
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}
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template<typename S>
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Vector2(const Vector<S, 2> &other)
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{
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Vector2 &v(*this);
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v(0) = static_cast<Type>(other(0));
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v(1) = static_cast<Type>(other(1));
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}
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using base = Vector<Type, 2>;
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using base::base;
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@@ -61,6 +69,17 @@ public:
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return (*this).cross(b);
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}
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operator Vector2<double>() const
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{
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return Vector2<double>((double)(*this)(0), (double)(*this)(1));
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}
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operator Vector2<float>() const
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{
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return Vector2<float>((float)(*this)(0), (float)(*this)(1));
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}
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};
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@@ -43,6 +43,24 @@ public:
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v(2) = z;
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}
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template<typename S>
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Vector3(const Vector<S, 3> &other)
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{
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Vector3 &v(*this);
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v(0) = static_cast<Type>(other(0));
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v(1) = static_cast<Type>(other(1));
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v(2) = static_cast<Type>(other(2));
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}
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template<typename S>
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Vector3(const Matrix<S, 3, 1> &other)
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{
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Vector3 &v(*this);
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v(0) = static_cast<Type>(other(0, 0));
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v(1) = static_cast<Type>(other(1, 0));
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v(2) = static_cast<Type>(other(2, 0));
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}
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using base = Vector<Type, 3>;
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using base::base;
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@@ -96,6 +114,21 @@ public:
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return (*this).cross(b);
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}
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operator Vector3<double>() const
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{
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return Vector3<double>((double)(*this)(0), (double)(*this)(1), (double)(*this)(2));
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}
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operator Vector3<float>() const
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{
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return Vector3<float>((float)(*this)(0), (float)(*this)(1), (float)(*this)(2));
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}
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ConstSlice<Type, 2, 1, 3, 1> xy() const
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{
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return {0, 0, this};
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@@ -224,6 +224,9 @@ px4_add_module(
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-fno-associative-math
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#-DDEBUG_BUILD
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#-O0
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-Wno-double-promotion
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#-Wno-error
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# single precision literal?
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INCLUDES
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EKF
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EKF/aid_sources
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@@ -141,4 +141,4 @@ add_library(ecl_EKF
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add_dependencies(ecl_EKF prebuild_targets)
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target_include_directories(ecl_EKF PUBLIC ${EKF_GENERATED_DERIVATION_INCLUDE_PATH})
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target_link_libraries(ecl_EKF PRIVATE geo world_magnetic_model)
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target_compile_options(ecl_EKF PRIVATE -fno-associative-math)
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target_compile_options(ecl_EKF PRIVATE -fno-associative-math -Wno-double-promotion -Wno-error)
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@@ -54,19 +54,19 @@ bool ZeroGyroUpdate::update(Ekf &ekf, const estimator::imuSample &imu_delayed)
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_zgup_delta_ang += imu_delayed.delta_ang;
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_zgup_delta_ang_dt += imu_delayed.delta_ang_dt;
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static constexpr float zgup_dt = 0.2f;
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static constexpr ekf_float_t zgup_dt = 0.2f;
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const bool zero_gyro_update_data_ready = _zgup_delta_ang_dt >= zgup_dt;
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if (zero_gyro_update_data_ready) {
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Vector3f gyro_bias = _zgup_delta_ang / _zgup_delta_ang_dt;
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Vector3<ekf_float_t> gyro_bias = _zgup_delta_ang / _zgup_delta_ang_dt;
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Vector3<ekf_float_t> innovation = ekf.state().gyro_bias - gyro_bias;
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const float obs_var = sq(math::constrain(ekf.getGyroNoise(), 0.f, 1.f));
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const ekf_float_t obs_var = sq(math::constrain(ekf.getGyroNoise(), 0.0001f, 1.f));
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for (unsigned i = 0; i < 3; i++) {
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const float innovation = ekf.state().gyro_bias(i) - gyro_bias(i);
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const float innov_var = ekf.getGyroBiasVariance()(i) + obs_var;
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ekf.fuseDirectStateMeasurement(innovation, innov_var, obs_var, State::gyro_bias.idx + i);
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ekf.fuseDirectStateMeasurement(innovation(i), innov_var, obs_var, State::gyro_bias.idx + i);
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}
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// Reset the integrators
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@@ -61,7 +61,7 @@ bool ZeroVelocityUpdate::update(Ekf &ekf, const estimator::imuSample &imu_delaye
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// Set a low variance initially for faster leveling and higher
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// later to let the states follow the measurements
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const float obs_var = ekf.control_status_flags().tilt_align ? sq(0.2f) : sq(0.001f);
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const ekf_float_t obs_var = ekf.control_status_flags().tilt_align ? sq(0.2f) : sq(0.001f);
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Vector3f innov_var = ekf.getVelocityVariance() + obs_var;
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for (unsigned i = 0; i < 3; i++) {
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@@ -143,12 +143,12 @@ void Ekf::updateAirspeed(const airspeedSample &airspeed_sample, estimator_aid_so
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resetEstimatorAidStatus(aid_src);
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// Variance for true airspeed measurement - (m/sec)^2
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const float R = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) *
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const ekf_float_t R = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) *
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math::constrain(airspeed_sample.eas2tas, 0.9f, 10.0f));
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float innov = 0.f;
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float innov_var = 0.f;
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sym::ComputeAirspeedInnovAndInnovVar(_state.vector(), P, airspeed_sample.true_airspeed, R, FLT_EPSILON, &innov, &innov_var);
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ekf_float_t innov = 0.f;
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ekf_float_t innov_var = 0.f;
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sym::ComputeAirspeedInnovAndInnovVar(_state.vector(), P, (ekf_float_t)airspeed_sample.true_airspeed, R, (ekf_float_t)FLT_EPSILON, &innov, &innov_var);
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aid_src.observation = airspeed_sample.true_airspeed;
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aid_src.observation_variance = R;
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@@ -199,10 +199,10 @@ void Ekf::fuseAirspeed(const airspeedSample &airspeed_sample, estimator_aid_sour
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VectorState H; // Observation jacobian
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VectorState K; // Kalman gain vector
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sym::ComputeAirspeedHAndK(_state.vector(), P, innov_var, FLT_EPSILON, &H, &K);
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sym::ComputeAirspeedHAndK(_state.vector(), P, (ekf_float_t)innov_var, (ekf_float_t)FLT_EPSILON, &H, &K);
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if (update_wind_only) {
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const Vector2f K_wind = K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0);
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const Vector2<ekf_float_t> K_wind = K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0);
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K.setZero();
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K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0) = K_wind;
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}
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@@ -232,13 +232,14 @@ void Ekf::stopAirspeedFusion()
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void Ekf::resetWindUsingAirspeed(const airspeedSample &airspeed_sample)
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{
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constexpr float sideslip_var = sq(math::radians(15.0f));
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ekf_float_t sideslip_var = sq(math::radians(15.0f));
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const float euler_yaw = getEulerYaw(_R_to_earth);
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const float airspeed_var = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) * math::constrain(airspeed_sample.eas2tas, 0.9f, 10.0f));
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const ekf_float_t euler_yaw = getEulerYaw(_R_to_earth);
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const ekf_float_t airspeed_var = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) * math::constrain(airspeed_sample.eas2tas, 0.9f, 10.0f));
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matrix::SquareMatrix<float, State::wind_vel.dof> P_wind;
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sym::ComputeWindInitAndCovFromAirspeed(_state.vel, euler_yaw, airspeed_sample.true_airspeed, getVelocityVariance(), getYawVar(), sideslip_var, airspeed_var, &_state.wind_vel, &P_wind);
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matrix::SquareMatrix<ekf_float_t, State::wind_vel.dof> P_wind;
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sym::ComputeWindInitAndCovFromAirspeed(_state.vel, euler_yaw, (ekf_float_t)airspeed_sample.true_airspeed, getVelocityVariance(), getYawVar(), sideslip_var, airspeed_var, &_state.wind_vel, &P_wind);
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resetStateCovariance<State::wind_vel>(P_wind);
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@@ -45,11 +45,18 @@
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#include <cstdint>
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#include <matrix/math.hpp>
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#include <mathlib/math/Functions.hpp>
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#include <mathlib/math/Utilities.hpp>
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namespace estimator
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{
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#if defined(CONFIG_EKF2_DOUBLE_PRECISION_FLOAT)
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using ekf_float_t = double;
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#else
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using ekf_float_t = float;
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#endif // CONFIG_EKF2_DOUBLE_PRECISION_FLOAT
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using matrix::AxisAnglef;
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using matrix::Dcmf;
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using matrix::Eulerf;
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@@ -57,12 +64,19 @@ using matrix::Matrix3f;
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using matrix::Quatf;
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using matrix::Vector2f;
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using matrix::Vector3f;
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using matrix::Dcm;
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using matrix::Vector2;
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using matrix::Vector3;
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using matrix::Quaternion;
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using matrix::AxisAngle;
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using matrix::wrap_pi;
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using math::Utilities::getEulerYaw;
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using math::Utilities::quatToInverseRotMat;
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using math::Utilities::shouldUse321RotationSequence;
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using math::Utilities::sq;
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using math::sq;
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using math::Utilities::updateYawInRotMat;
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// maximum sensor intervals in usec
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@@ -104,15 +104,15 @@ void Ekf::initialiseCovariance()
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void Ekf::predictCovariance(const imuSample &imu_delayed)
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{
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// predict the covariance
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const float dt = 0.5f * (imu_delayed.delta_vel_dt + imu_delayed.delta_ang_dt);
|
||||
const ekf_float_t dt = 0.5f * (imu_delayed.delta_vel_dt + imu_delayed.delta_ang_dt);
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||||
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||||
// gyro noise variance
|
||||
float gyro_noise = math::constrain(_params.gyro_noise, 0.f, 1.f);
|
||||
const float gyro_var = sq(gyro_noise);
|
||||
ekf_float_t gyro_noise = math::constrain(_params.gyro_noise, 0.f, 1.f);
|
||||
const ekf_float_t gyro_var = sq(gyro_noise);
|
||||
|
||||
// accel noise variance
|
||||
float accel_noise = math::constrain(_params.accel_noise, 0.f, 1.f);
|
||||
Vector3f accel_var;
|
||||
ekf_float_t accel_noise = math::constrain(_params.accel_noise, 0.f, 1.f);
|
||||
Vector3<ekf_float_t> accel_var;
|
||||
|
||||
for (unsigned i = 0; i < 3; i++) {
|
||||
if (_fault_status.flags.bad_acc_vertical || imu_delayed.delta_vel_clipping[i]) {
|
||||
@@ -125,9 +125,11 @@ void Ekf::predictCovariance(const imuSample &imu_delayed)
|
||||
}
|
||||
|
||||
// calculate variances and upper diagonal covariances for quaternion, velocity, position and gyro bias states
|
||||
const Vector3<ekf_float_t> accel = imu_delayed.delta_vel / math::max(imu_delayed.delta_vel_dt, FLT_EPSILON);
|
||||
const Vector3<ekf_float_t> gyro = imu_delayed.delta_ang / math::max(imu_delayed.delta_ang_dt, FLT_EPSILON);
|
||||
P = sym::PredictCovariance(_state.vector(), P,
|
||||
imu_delayed.delta_vel / math::max(imu_delayed.delta_vel_dt, FLT_EPSILON), accel_var,
|
||||
imu_delayed.delta_ang / math::max(imu_delayed.delta_ang_dt, FLT_EPSILON), gyro_var,
|
||||
accel, accel_var,
|
||||
gyro, gyro_var,
|
||||
dt);
|
||||
|
||||
// Construct the process noise variance diagonal for those states with a stationary process model
|
||||
|
||||
@@ -63,12 +63,12 @@ void Ekf::controlDragFusion(const imuSample &imu_delayed)
|
||||
|
||||
void Ekf::fuseDrag(const dragSample &drag_sample)
|
||||
{
|
||||
const float R_ACC = fmaxf(_params.drag_noise, 0.5f); // observation noise variance in specific force drag (m/sec**2)**2
|
||||
const float rho = fmaxf(_air_density, 0.1f); // air density (kg/m**3)
|
||||
const ekf_float_t R_ACC = math::max(_params.drag_noise, 0.5f); // observation noise variance in specific force drag (m/sec**2)**2
|
||||
const ekf_float_t rho = math::max(_air_density, 0.1f); // air density (kg/m**3)
|
||||
|
||||
// correct rotor momentum drag for increase in required rotor mass flow with altitude
|
||||
// obtained from momentum disc theory
|
||||
const float mcoef_corrrected = fmaxf(_params.mcoef * sqrtf(rho / atmosphere::kAirDensitySeaLevelStandardAtmos), 0.f);
|
||||
const ekf_float_t mcoef_corrrected = math::max(_params.mcoef * sqrtf(rho / atmosphere::kAirDensitySeaLevelStandardAtmos), 0.f);
|
||||
|
||||
// drag model parameters
|
||||
const bool using_bcoef_x = _params.bcoef_x > 1.0f;
|
||||
@@ -80,14 +80,14 @@ void Ekf::fuseDrag(const dragSample &drag_sample)
|
||||
}
|
||||
|
||||
// calculate relative wind velocity in earth frame and rotate into body frame
|
||||
const Vector3f rel_wind_earth(_state.vel(0) - _state.wind_vel(0),
|
||||
const Vector3<ekf_float_t> rel_wind_earth(_state.vel(0) - _state.wind_vel(0),
|
||||
_state.vel(1) - _state.wind_vel(1),
|
||||
_state.vel(2));
|
||||
const Vector3f rel_wind_body = _state.quat_nominal.rotateVectorInverse(rel_wind_earth);
|
||||
const float rel_wind_speed = rel_wind_body.norm();
|
||||
const Vector3<ekf_float_t> rel_wind_body = _state.quat_nominal.rotateVectorInverse(rel_wind_earth);
|
||||
const ekf_float_t rel_wind_speed = rel_wind_body.norm();
|
||||
const auto state_vector_prev = _state.vector();
|
||||
|
||||
Vector2f bcoef_inv{0.f, 0.f};
|
||||
Vector2<ekf_float_t> bcoef_inv{0.f, 0.f};
|
||||
|
||||
if (using_bcoef_x) {
|
||||
bcoef_inv(0) = 1.f / _params.bcoef_x;
|
||||
@@ -101,7 +101,7 @@ void Ekf::fuseDrag(const dragSample &drag_sample)
|
||||
|
||||
// Interpolate between the X and Y bluff body drag coefficients using current relative velocity
|
||||
// This creates an elliptic drag distribution around the XY plane
|
||||
bcoef_inv(0) = Vector2f(bcoef_inv.emult(rel_wind_body.xy()) / rel_wind_body.xy().norm()).norm();
|
||||
bcoef_inv(0) = Vector2<ekf_float_t>(bcoef_inv.emult(rel_wind_body.xy()) / rel_wind_body.xy().norm()).norm();
|
||||
bcoef_inv(1) = bcoef_inv(0);
|
||||
}
|
||||
|
||||
@@ -115,12 +115,12 @@ void Ekf::fuseDrag(const dragSample &drag_sample)
|
||||
// perform sequential fusion of XY specific forces
|
||||
for (uint8_t axis_index = 0; axis_index < 2; axis_index++) {
|
||||
// measured drag acceleration corrected for sensor bias
|
||||
const float mea_acc = drag_sample.accelXY(axis_index) - _state.accel_bias(axis_index);
|
||||
const ekf_float_t mea_acc = drag_sample.accelXY(axis_index) - _state.accel_bias(axis_index);
|
||||
|
||||
// Drag is modelled as an arbitrary combination of bluff body drag that proportional to
|
||||
// equivalent airspeed squared, and rotor momentum drag that is proportional to true airspeed
|
||||
// parallel to the rotor disc and mass flow through the rotor disc.
|
||||
const float pred_acc = -0.5f * bcoef_inv(axis_index) * rho * rel_wind_body(axis_index) * rel_wind_speed - rel_wind_body(axis_index) * mcoef_corrrected;
|
||||
const ekf_float_t pred_acc = -0.5f * bcoef_inv(axis_index) * rho * rel_wind_body(axis_index) * rel_wind_speed - rel_wind_body(axis_index) * mcoef_corrrected;
|
||||
|
||||
_aid_src_drag.observation[axis_index] = mea_acc;
|
||||
_aid_src_drag.observation_variance[axis_index] = R_ACC;
|
||||
@@ -128,16 +128,22 @@ void Ekf::fuseDrag(const dragSample &drag_sample)
|
||||
_aid_src_drag.innovation_variance[axis_index] = NAN; // reset
|
||||
|
||||
if (axis_index == 0) {
|
||||
sym::ComputeDragXInnovVarAndH(state_vector_prev, P, rho, bcoef_inv(axis_index), mcoef_corrrected, R_ACC, FLT_EPSILON,
|
||||
&_aid_src_drag.innovation_variance[axis_index], &H);
|
||||
ekf_float_t innovation_variance = 0;
|
||||
sym::ComputeDragXInnovVarAndH(state_vector_prev, P, rho, bcoef_inv(axis_index), mcoef_corrrected, R_ACC, (ekf_float_t)FLT_EPSILON,
|
||||
&innovation_variance, &H);
|
||||
|
||||
_aid_src_drag.innovation_variance[axis_index] = innovation_variance;
|
||||
|
||||
if (!using_bcoef_x && !using_mcoef) {
|
||||
continue;
|
||||
}
|
||||
|
||||
} else if (axis_index == 1) {
|
||||
sym::ComputeDragYInnovVarAndH(state_vector_prev, P, rho, bcoef_inv(axis_index), mcoef_corrrected, R_ACC, FLT_EPSILON,
|
||||
&_aid_src_drag.innovation_variance[axis_index], &H);
|
||||
ekf_float_t innovation_variance = 0;
|
||||
sym::ComputeDragYInnovVarAndH(state_vector_prev, P, rho, bcoef_inv(axis_index), mcoef_corrrected, R_ACC, (ekf_float_t)FLT_EPSILON,
|
||||
&innovation_variance, &H);
|
||||
|
||||
_aid_src_drag.innovation_variance[axis_index] = innovation_variance;
|
||||
|
||||
if (!using_bcoef_y && !using_mcoef) {
|
||||
continue;
|
||||
|
||||
@@ -280,17 +280,17 @@ bool Ekf::initialiseTilt()
|
||||
void Ekf::predictState(const imuSample &imu_delayed)
|
||||
{
|
||||
// apply imu bias corrections
|
||||
const Vector3f delta_ang_bias_scaled = getGyroBias() * imu_delayed.delta_ang_dt;
|
||||
Vector3f corrected_delta_ang = imu_delayed.delta_ang - delta_ang_bias_scaled;
|
||||
const Vector3<ekf_float_t> delta_ang_bias_scaled = getGyroBias() * imu_delayed.delta_ang_dt;
|
||||
Vector3<ekf_float_t> corrected_delta_ang = imu_delayed.delta_ang - delta_ang_bias_scaled;
|
||||
|
||||
// subtract component of angular rate due to earth rotation
|
||||
corrected_delta_ang -= _R_to_earth.transpose() * _earth_rate_NED * imu_delayed.delta_ang_dt;
|
||||
|
||||
const Quatf dq(AxisAnglef{corrected_delta_ang});
|
||||
const Quaternion<ekf_float_t> dq(AxisAngle<ekf_float_t>{corrected_delta_ang});
|
||||
|
||||
// rotate the previous quaternion by the delta quaternion using a quaternion multiplication
|
||||
_state.quat_nominal = (_state.quat_nominal * dq).normalized();
|
||||
_R_to_earth = Dcmf(_state.quat_nominal);
|
||||
_R_to_earth = Dcm<ekf_float_t>(_state.quat_nominal);
|
||||
|
||||
// Calculate an earth frame delta velocity
|
||||
const Vector3f delta_vel_bias_scaled = getAccelBias() * imu_delayed.delta_vel_dt;
|
||||
@@ -298,7 +298,7 @@ void Ekf::predictState(const imuSample &imu_delayed)
|
||||
const Vector3f corrected_delta_vel_ef = _R_to_earth * corrected_delta_vel;
|
||||
|
||||
// save the previous value of velocity so we can use trapzoidal integration
|
||||
const Vector3f vel_last = _state.vel;
|
||||
const auto vel_last = _state.vel;
|
||||
|
||||
// calculate the increment in velocity using the current orientation
|
||||
_state.vel += corrected_delta_vel_ef;
|
||||
@@ -310,8 +310,8 @@ void Ekf::predictState(const imuSample &imu_delayed)
|
||||
_state.pos += (vel_last + _state.vel) * imu_delayed.delta_vel_dt * 0.5f;
|
||||
|
||||
// constrain states
|
||||
_state.vel = matrix::constrain(_state.vel, -1000.f, 1000.f);
|
||||
_state.pos = matrix::constrain(_state.pos, -1.e6f, 1.e6f);
|
||||
_state.vel = matrix::constrain(_state.vel, (ekf_float_t)-1000., (ekf_float_t)1000.);
|
||||
_state.pos = matrix::constrain(_state.pos, (ekf_float_t)-1.e6, (ekf_float_t)1.e6);
|
||||
|
||||
// some calculations elsewhere in code require a raw angular rate vector so calculate here to avoid duplication
|
||||
// protect against possible small timesteps resulting from timing slip on previous frame that can drive spikes into the rate
|
||||
@@ -343,7 +343,8 @@ void Ekf::resetGlobalPosToExternalObservation(double lat_deg, double lon_deg, fl
|
||||
const float dt = _time_delayed_us > timestamp_observation ? static_cast<float>(_time_delayed_us - timestamp_observation)
|
||||
* 1e-6f : -static_cast<float>(timestamp_observation - _time_delayed_us) * 1e-6f;
|
||||
|
||||
Vector2f pos_corrected = _pos_ref.project(lat_deg, lon_deg) + _state.vel.xy() * dt;
|
||||
Vector2f vel_xy(_state.vel(0), _state.vel(1));
|
||||
Vector2f pos_corrected = _pos_ref.project(lat_deg, lon_deg) + vel_xy * dt;
|
||||
|
||||
resetHorizontalPositionToExternal(pos_corrected, math::max(accuracy, FLT_EPSILON));
|
||||
}
|
||||
|
||||
+29
-30
@@ -71,9 +71,8 @@ enum class Likelihood { LOW, MEDIUM, HIGH };
|
||||
class Ekf final : public EstimatorInterface
|
||||
{
|
||||
public:
|
||||
typedef matrix::Vector<float, State::size> VectorState;
|
||||
typedef matrix::SquareMatrix<float, State::size> SquareMatrixState;
|
||||
typedef matrix::SquareMatrix<float, 2> Matrix2f;
|
||||
typedef matrix::Vector<ekf_float_t, State::size> VectorState;
|
||||
typedef matrix::SquareMatrix<ekf_float_t, State::size> SquareMatrixState;
|
||||
|
||||
Ekf()
|
||||
{
|
||||
@@ -237,31 +236,31 @@ public:
|
||||
|
||||
#if defined(CONFIG_EKF2_WIND)
|
||||
// get the wind velocity in m/s
|
||||
const Vector2f &getWindVelocity() const { return _state.wind_vel; };
|
||||
Vector2f getWindVelocityVariance() const { return getStateVariance<State::wind_vel>(); }
|
||||
const auto &getWindVelocity() const { return _state.wind_vel; };
|
||||
auto getWindVelocityVariance() const { return getStateVariance<State::wind_vel>(); }
|
||||
#endif // CONFIG_EKF2_WIND
|
||||
|
||||
template <const IdxDof &S>
|
||||
matrix::Vector<float, S.dof>getStateVariance() const { return P.slice<S.dof, S.dof>(S.idx, S.idx).diag(); } // calling getStateCovariance().diag() uses more flash space
|
||||
matrix::Vector<ekf_float_t, S.dof>getStateVariance() const { return P.slice<S.dof, S.dof>(S.idx, S.idx).diag(); } // calling getStateCovariance().diag() uses more flash space
|
||||
|
||||
template <const IdxDof &S>
|
||||
matrix::SquareMatrix<float, S.dof>getStateCovariance() const { return P.slice<S.dof, S.dof>(S.idx, S.idx); }
|
||||
matrix::SquareMatrix<ekf_float_t, S.dof>getStateCovariance() const { return P.slice<S.dof, S.dof>(S.idx, S.idx); }
|
||||
|
||||
// get the full covariance matrix
|
||||
const matrix::SquareMatrix<float, State::size> &covariances() const { return P; }
|
||||
float stateCovariance(unsigned r, unsigned c) const { return P(r, c); }
|
||||
const matrix::SquareMatrix<ekf_float_t, State::size> &covariances() const { return P; }
|
||||
const auto &stateCovariance(unsigned r, unsigned c) const { return P(r, c); }
|
||||
|
||||
// get the diagonal elements of the covariance matrix
|
||||
matrix::Vector<float, State::size> covariances_diagonal() const { return P.diag(); }
|
||||
matrix::Vector<ekf_float_t, State::size> covariances_diagonal() const { return P.diag(); }
|
||||
|
||||
matrix::Vector<float, State::quat_nominal.dof> getQuaternionVariance() const { return getStateVariance<State::quat_nominal>(); }
|
||||
matrix::Vector3f getRotVarNed() const;
|
||||
float getYawVar() const;
|
||||
float getTiltVariance() const;
|
||||
matrix::Vector<ekf_float_t, State::quat_nominal.dof> getQuaternionVariance() const { return getStateVariance<State::quat_nominal>(); }
|
||||
matrix::Vector3<ekf_float_t> getRotVarNed() const;
|
||||
ekf_float_t getYawVar() const;
|
||||
ekf_float_t getTiltVariance() const;
|
||||
|
||||
Vector3f getVelocityVariance() const { return getStateVariance<State::vel>(); };
|
||||
auto getVelocityVariance() const { return getStateVariance<State::vel>(); };
|
||||
|
||||
Vector3f getPositionVariance() const { return getStateVariance<State::pos>(); }
|
||||
auto getPositionVariance() const { return getStateVariance<State::pos>(); }
|
||||
|
||||
// get the ekf WGS-84 origin position and height and the system time it was last set
|
||||
// return true if the origin is valid
|
||||
@@ -331,21 +330,21 @@ public:
|
||||
bool fuseDirectStateMeasurement(const float innov, const float innov_var, const float R, const int state_index);
|
||||
|
||||
// gyro bias
|
||||
const Vector3f &getGyroBias() const { return _state.gyro_bias; } // get the gyroscope bias in rad/s
|
||||
Vector3f getGyroBiasVariance() const { return getStateVariance<State::gyro_bias>(); } // get the gyroscope bias variance in rad/s
|
||||
const auto &getGyroBias() const { return _state.gyro_bias; } // get the gyroscope bias in rad/s
|
||||
auto getGyroBiasVariance() const { return getStateVariance<State::gyro_bias>(); } // get the gyroscope bias variance in rad/s
|
||||
float getGyroBiasLimit() const { return _params.gyro_bias_lim; }
|
||||
float getGyroNoise() const { return _params.gyro_noise; }
|
||||
|
||||
// accel bias
|
||||
const Vector3f &getAccelBias() const { return _state.accel_bias; } // get the accelerometer bias in m/s**2
|
||||
Vector3f getAccelBiasVariance() const { return getStateVariance<State::accel_bias>(); } // get the accelerometer bias variance in m/s**2
|
||||
const auto &getAccelBias() const { return _state.accel_bias; } // get the accelerometer bias in m/s**2
|
||||
auto getAccelBiasVariance() const { return getStateVariance<State::accel_bias>(); } // get the accelerometer bias variance in m/s**2
|
||||
float getAccelBiasLimit() const { return _params.acc_bias_lim; }
|
||||
|
||||
#if defined(CONFIG_EKF2_MAGNETOMETER)
|
||||
const Vector3f &getMagEarthField() const { return _state.mag_I; }
|
||||
const auto &getMagEarthField() const { return _state.mag_I; }
|
||||
|
||||
const Vector3f &getMagBias() const { return _state.mag_B; }
|
||||
Vector3f getMagBiasVariance() const { return getStateVariance<State::mag_B>(); } // get the mag bias variance in Gauss
|
||||
const auto &getMagBias() const { return _state.mag_B; }
|
||||
auto getMagBiasVariance() const { return getStateVariance<State::mag_B>(); } // get the mag bias variance in Gauss
|
||||
float getMagBiasLimit() const { return 0.5f; } // 0.5 Gauss
|
||||
#endif // CONFIG_EKF2_MAGNETOMETER
|
||||
|
||||
@@ -468,14 +467,14 @@ public:
|
||||
const auto &aid_src_aux_vel() const { return _aid_src_aux_vel; }
|
||||
#endif // CONFIG_EKF2_AUXVEL
|
||||
|
||||
bool measurementUpdate(VectorState &K, const VectorState &H, const float R, const float innovation)
|
||||
bool measurementUpdate(VectorState &K, const VectorState &H, const ekf_float_t R, const ekf_float_t innovation)
|
||||
{
|
||||
clearInhibitedStateKalmanGains(K);
|
||||
|
||||
#if false
|
||||
// Matrix implementation of the Joseph stabilized covariance update
|
||||
// This is extremely expensive to compute. Use for debugging purposes only.
|
||||
auto A = matrix::eye<float, State::size>();
|
||||
auto A = matrix::eye<ekf_float_t, State::size>();
|
||||
A -= K.multiplyByTranspose(H);
|
||||
P = A * P;
|
||||
P = P.multiplyByTranspose(A);
|
||||
@@ -536,8 +535,8 @@ private:
|
||||
void updateHorizontalDeadReckoningstatus();
|
||||
void updateVerticalDeadReckoningStatus();
|
||||
|
||||
static constexpr float kGyroBiasVarianceMin{1e-9f};
|
||||
static constexpr float kAccelBiasVarianceMin{1e-9f};
|
||||
static constexpr float kGyroBiasVarianceMin{1e-12f};
|
||||
static constexpr float kAccelBiasVarianceMin{1e-12f};
|
||||
|
||||
#if defined(CONFIG_EKF2_MAGNETOMETER)
|
||||
static constexpr float kMagVarianceMin = 1e-6f;
|
||||
@@ -581,7 +580,7 @@ private:
|
||||
uint64_t _time_last_ver_vel_fuse{0}; ///< time the last fusion of verticalvelocity measurements was performed (uSec)
|
||||
uint64_t _time_last_heading_fuse{0};
|
||||
|
||||
Vector3f _last_known_pos{}; ///< last known local position vector (m)
|
||||
Vector3<ekf_float_t> _last_known_pos{}; ///< last known local position vector (m)
|
||||
|
||||
uint64_t _time_acc_bias_check{0}; ///< last time the accel bias check passed (uSec)
|
||||
|
||||
@@ -762,7 +761,7 @@ private:
|
||||
void predictCovariance(const imuSample &imu_delayed);
|
||||
|
||||
template <const IdxDof &S>
|
||||
void resetStateCovariance(const matrix::SquareMatrix<float, S.dof> &cov)
|
||||
void resetStateCovariance(const matrix::SquareMatrix<ekf_float_t, S.dof> &cov)
|
||||
{
|
||||
P.uncorrelateCovarianceSetVariance<S.dof>(S.idx, 0.0f);
|
||||
P.slice<S.dof, S.dof>(S.idx, S.idx) = cov;
|
||||
@@ -771,7 +770,7 @@ private:
|
||||
// update quaternion states and covariances using an innovation, observation variance and Jacobian vector
|
||||
bool fuseYaw(estimator_aid_source1d_s &aid_src_status);
|
||||
bool fuseYaw(estimator_aid_source1d_s &aid_src_status, const VectorState &H_YAW);
|
||||
void computeYawInnovVarAndH(float variance, float &innovation_variance, VectorState &H_YAW) const;
|
||||
void computeYawInnovVarAndH(ekf_float_t variance, ekf_float_t &innovation_variance, VectorState &H_YAW) const;
|
||||
|
||||
void updateIMUBiasInhibit(const imuSample &imu_delayed);
|
||||
|
||||
|
||||
@@ -557,31 +557,31 @@ void Ekf::fuse(const VectorState &K, float innovation)
|
||||
_R_to_earth = Dcmf(_state.quat_nominal);
|
||||
|
||||
// vel
|
||||
_state.vel = matrix::constrain(_state.vel - K.slice<State::vel.dof, 1>(State::vel.idx, 0) * innovation, -1.e3f, 1.e3f);
|
||||
_state.vel = matrix::constrain(_state.vel - K.slice<State::vel.dof, 1>(State::vel.idx, 0) * innovation, (ekf_float_t)-1.e3, (ekf_float_t)1.e3);
|
||||
|
||||
// pos
|
||||
_state.pos = matrix::constrain(_state.pos - K.slice<State::pos.dof, 1>(State::pos.idx, 0) * innovation, -1.e6f, 1.e6f);
|
||||
_state.pos = matrix::constrain(_state.pos - K.slice<State::pos.dof, 1>(State::pos.idx, 0) * innovation, (ekf_float_t)-1.e6, (ekf_float_t)1.e6);
|
||||
|
||||
// gyro_bias
|
||||
_state.gyro_bias = matrix::constrain(_state.gyro_bias - K.slice<State::gyro_bias.dof, 1>(State::gyro_bias.idx, 0) * innovation,
|
||||
-getGyroBiasLimit(), getGyroBiasLimit());
|
||||
(ekf_float_t)-getGyroBiasLimit(), (ekf_float_t)getGyroBiasLimit());
|
||||
|
||||
// accel_bias
|
||||
_state.accel_bias = matrix::constrain(_state.accel_bias - K.slice<State::accel_bias.dof, 1>(State::accel_bias.idx, 0) * innovation,
|
||||
-getAccelBiasLimit(), getAccelBiasLimit());
|
||||
(ekf_float_t)-getAccelBiasLimit(), (ekf_float_t)getAccelBiasLimit());
|
||||
|
||||
#if defined(CONFIG_EKF2_MAGNETOMETER)
|
||||
// mag_I, mag_B
|
||||
if (_control_status.flags.mag) {
|
||||
_state.mag_I = matrix::constrain(_state.mag_I - K.slice<State::mag_I.dof, 1>(State::mag_I.idx, 0) * innovation, -1.f, 1.f);
|
||||
_state.mag_B = matrix::constrain(_state.mag_B - K.slice<State::mag_B.dof, 1>(State::mag_B.idx, 0) * innovation, -getMagBiasLimit(), getMagBiasLimit());
|
||||
_state.mag_I = matrix::constrain(_state.mag_I - K.slice<State::mag_I.dof, 1>(State::mag_I.idx, 0) * innovation, (ekf_float_t)-1., (ekf_float_t)1.);
|
||||
_state.mag_B = matrix::constrain(_state.mag_B - K.slice<State::mag_B.dof, 1>(State::mag_B.idx, 0) * innovation, (ekf_float_t)-getMagBiasLimit(), (ekf_float_t)getMagBiasLimit());
|
||||
}
|
||||
#endif // CONFIG_EKF2_MAGNETOMETER
|
||||
|
||||
#if defined(CONFIG_EKF2_WIND)
|
||||
// wind_vel
|
||||
if (_control_status.flags.wind) {
|
||||
_state.wind_vel = matrix::constrain(_state.wind_vel - K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0) * innovation, -1.e2f, 1.e2f);
|
||||
_state.wind_vel = matrix::constrain(_state.wind_vel - K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0) * innovation, (ekf_float_t)-1.e2, (ekf_float_t)1.e2);
|
||||
}
|
||||
#endif // CONFIG_EKF2_WIND
|
||||
}
|
||||
@@ -660,20 +660,20 @@ void Ekf::updateVerticalDeadReckoningStatus()
|
||||
}
|
||||
}
|
||||
|
||||
Vector3f Ekf::getRotVarNed() const
|
||||
Vector3<ekf_float_t> Ekf::getRotVarNed() const
|
||||
{
|
||||
const matrix::SquareMatrix3f rot_cov_body = getStateCovariance<State::quat_nominal>();
|
||||
return matrix::SquareMatrix<float, State::quat_nominal.dof>(_R_to_earth * rot_cov_body * _R_to_earth.T()).diag();
|
||||
return matrix::SquareMatrix<ekf_float_t, State::quat_nominal.dof>(_R_to_earth * rot_cov_body * _R_to_earth.T()).diag();
|
||||
}
|
||||
|
||||
float Ekf::getYawVar() const
|
||||
ekf_float_t Ekf::getYawVar() const
|
||||
{
|
||||
return getRotVarNed()(2);
|
||||
}
|
||||
|
||||
float Ekf::getTiltVariance() const
|
||||
ekf_float_t Ekf::getTiltVariance() const
|
||||
{
|
||||
const Vector3f rot_var_ned = getRotVarNed();
|
||||
const Vector3<ekf_float_t> rot_var_ned = getRotVarNed();
|
||||
return rot_var_ned(0) + rot_var_ned(1);
|
||||
}
|
||||
|
||||
@@ -708,7 +708,7 @@ void Ekf::resetQuatStateYaw(float yaw, float yaw_variance)
|
||||
const Quatf quat_before_reset = _state.quat_nominal;
|
||||
|
||||
// save a copy of covariance in NED frame to restore it after the quat reset
|
||||
Vector3f rot_var_ned_before_reset = getRotVarNed();
|
||||
Vector3<ekf_float_t> rot_var_ned_before_reset = getRotVarNed();
|
||||
|
||||
// update the yaw angle variance
|
||||
if (PX4_ISFINITE(yaw_variance) && (yaw_variance > FLT_EPSILON)) {
|
||||
|
||||
@@ -91,7 +91,8 @@ void Ekf::controlExternalVisionFusion()
|
||||
|
||||
void Ekf::updateEvAttitudeErrorFilter(extVisionSample &ev_sample, bool ev_reset)
|
||||
{
|
||||
const Quatf q_error((_state.quat_nominal * ev_sample.quat.inversed()).normalized());
|
||||
const Quatf q_nominal = _state.quat_nominal;
|
||||
const Quatf q_error((q_nominal * ev_sample.quat.inversed()).normalized());
|
||||
|
||||
if (!q_error.isAllFinite()) {
|
||||
return;
|
||||
|
||||
@@ -73,8 +73,8 @@ void Ekf::controlEvHeightFusion(const extVisionSample &ev_sample, const bool com
|
||||
}
|
||||
}
|
||||
|
||||
const float measurement = pos(2) - pos_offset_earth(2);
|
||||
float measurement_var = math::max(pos_cov(2, 2), sq(_params.ev_pos_noise), sq(0.01f));
|
||||
const ekf_float_t measurement = pos(2) - pos_offset_earth(2);
|
||||
ekf_float_t measurement_var = math::max(pos_cov(2, 2), sq(_params.ev_pos_noise), sq(0.01f));
|
||||
|
||||
#if defined(CONFIG_EKF2_GNSS)
|
||||
// increase minimum variance if GPS active
|
||||
|
||||
@@ -147,7 +147,8 @@ void Ekf::controlEvPosFusion(const extVisionSample &ev_sample, const bool common
|
||||
if (!bias_fusion_was_active && _ev_pos_b_est.fusionActive()) {
|
||||
if (quality_sufficient) {
|
||||
// reset the bias estimator
|
||||
_ev_pos_b_est.setBias(-Vector2f(_state.pos.xy()) + measurement);
|
||||
Vector2f pos_xy(-_state.pos(0), -_state.pos(1));
|
||||
_ev_pos_b_est.setBias(-pos_xy + measurement);
|
||||
|
||||
} else if (isOtherSourceOfHorizontalAidingThan(_control_status.flags.ev_pos)) {
|
||||
// otherwise stop EV position, when quality is good again it will restart with reset bias
|
||||
@@ -166,7 +167,10 @@ void Ekf::controlEvPosFusion(const extVisionSample &ev_sample, const bool common
|
||||
if (measurement_valid && quality_sufficient) {
|
||||
_ev_pos_b_est.setMaxStateNoise(Vector2f(sqrtf(measurement_var(0)), sqrtf(measurement_var(1))));
|
||||
_ev_pos_b_est.setProcessNoiseSpectralDensity(_params.ev_hgt_bias_nsd); // TODO
|
||||
_ev_pos_b_est.fuseBias(measurement - Vector2f(_state.pos.xy()), measurement_var + Vector2f(getStateVariance<State::pos>()));
|
||||
|
||||
Vector2f pos_xy(-_state.pos(0), -_state.pos(1));
|
||||
Vector2f pos_var(getStateVariance<State::pos>()(0), getStateVariance<State::pos>()(1));
|
||||
_ev_pos_b_est.fuseBias(measurement - pos_xy, measurement_var + pos_var);
|
||||
}
|
||||
|
||||
if (!measurement_valid) {
|
||||
@@ -203,7 +207,9 @@ void Ekf::startEvPosFusion(const Vector2f &measurement, const Vector2f &measurem
|
||||
// TODO: (_params.position_sensor_ref == PositionSensor::EV)
|
||||
if (_control_status.flags.gps) {
|
||||
ECL_INFO("starting %s fusion", EV_AID_SRC_NAME);
|
||||
_ev_pos_b_est.setBias(-Vector2f(_state.pos.xy()) + measurement);
|
||||
|
||||
Vector2f pos_xy(-_state.pos(0), -_state.pos(1));
|
||||
_ev_pos_b_est.setBias(-pos_xy + measurement);
|
||||
_ev_pos_b_est.setFusionActive();
|
||||
|
||||
} else {
|
||||
@@ -234,7 +240,8 @@ void Ekf::updateEvPosFusion(const Vector2f &measurement, const Vector2f &measure
|
||||
_ev_pos_b_est.reset();
|
||||
|
||||
} else {
|
||||
_ev_pos_b_est.setBias(-Vector2f(_state.pos.xy()) + measurement);
|
||||
Vector2f pos_xy(-_state.pos(0), -_state.pos(1));
|
||||
_ev_pos_b_est.setBias(-pos_xy + measurement);
|
||||
}
|
||||
|
||||
aid_src.time_last_fuse = _time_delayed_us;
|
||||
@@ -264,14 +271,16 @@ void Ekf::updateEvPosFusion(const Vector2f &measurement, const Vector2f &measure
|
||||
|
||||
if (_control_status.flags.gps && !pos_xy_fusion_failing) {
|
||||
// reset EV position bias
|
||||
_ev_pos_b_est.setBias(-Vector2f(_state.pos.xy()) + measurement);
|
||||
Vector2f pos_xy(-_state.pos(0), -_state.pos(1));
|
||||
_ev_pos_b_est.setBias(-pos_xy + measurement);
|
||||
|
||||
} else {
|
||||
_information_events.flags.reset_pos_to_vision = true;
|
||||
|
||||
if (_control_status.flags.gps) {
|
||||
resetHorizontalPositionTo(measurement - _ev_pos_b_est.getBias(), measurement_var + _ev_pos_b_est.getBiasVar());
|
||||
_ev_pos_b_est.setBias(-Vector2f(_state.pos.xy()) + measurement);
|
||||
Vector2f pos_xy(-_state.pos(0), -_state.pos(1));
|
||||
_ev_pos_b_est.setBias(-pos_xy + measurement);
|
||||
|
||||
} else {
|
||||
resetHorizontalPositionTo(measurement, measurement_var);
|
||||
|
||||
@@ -56,17 +56,17 @@ void Ekf::updateGpsYaw(const gnssSample &gps_sample)
|
||||
// initially populate for estimator_aid_src_gnss_yaw logging
|
||||
|
||||
// calculate the observed yaw angle of antenna array, converting a from body to antenna yaw measurement
|
||||
const float measured_hdg = wrap_pi(gps_sample.yaw + gps_sample.yaw_offset);
|
||||
const ekf_float_t measured_hdg = wrap_pi(gps_sample.yaw + gps_sample.yaw_offset);
|
||||
|
||||
const float yaw_acc = PX4_ISFINITE(gps_sample.yaw_acc) ? gps_sample.yaw_acc : 0.f;
|
||||
const float R_YAW = sq(fmaxf(yaw_acc, _params.gps_heading_noise));
|
||||
const ekf_float_t yaw_acc = PX4_ISFINITE(gps_sample.yaw_acc) ? gps_sample.yaw_acc : 0.f;
|
||||
const ekf_float_t R_YAW = sq(fmaxf(yaw_acc, _params.gps_heading_noise));
|
||||
|
||||
float heading_pred;
|
||||
float heading_innov_var;
|
||||
ekf_float_t heading_pred;
|
||||
ekf_float_t heading_innov_var;
|
||||
|
||||
{
|
||||
VectorState H;
|
||||
sym::ComputeGnssYawPredInnovVarAndH(_state.vector(), P, gps_sample.yaw_offset, R_YAW, FLT_EPSILON, &heading_pred, &heading_innov_var, &H);
|
||||
sym::ComputeGnssYawPredInnovVarAndH(_state.vector(), P, (ekf_float_t)gps_sample.yaw_offset, R_YAW, (ekf_float_t)FLT_EPSILON, &heading_pred, &heading_innov_var, &H);
|
||||
}
|
||||
|
||||
gnss_yaw.observation = measured_hdg;
|
||||
@@ -97,12 +97,12 @@ void Ekf::fuseGpsYaw(float antenna_yaw_offset)
|
||||
VectorState H;
|
||||
|
||||
{
|
||||
float heading_pred;
|
||||
float heading_innov_var;
|
||||
ekf_float_t heading_pred;
|
||||
ekf_float_t heading_innov_var;
|
||||
|
||||
// Note: we recompute innov and innov_var because it doesn't cost much more than just computing H
|
||||
// making a separate function just for H uses more flash space without reducing CPU load significantly
|
||||
sym::ComputeGnssYawPredInnovVarAndH(_state.vector(), P, antenna_yaw_offset, gnss_yaw.observation_variance, FLT_EPSILON, &heading_pred, &heading_innov_var, &H);
|
||||
sym::ComputeGnssYawPredInnovVarAndH(_state.vector(), P, (ekf_float_t)antenna_yaw_offset, (ekf_float_t)gnss_yaw.observation_variance, (ekf_float_t)FLT_EPSILON, &heading_pred, &heading_innov_var, &H);
|
||||
}
|
||||
|
||||
// check if the innovation variance calculation is badly conditioned
|
||||
|
||||
@@ -50,7 +50,7 @@ void Ekf::controlGravityFusion(const imuSample &imu)
|
||||
{
|
||||
// get raw accelerometer reading at delayed horizon and expected measurement noise (gaussian)
|
||||
const Vector3f measurement = Vector3f(imu.delta_vel / imu.delta_vel_dt - _state.accel_bias).unit();
|
||||
const float measurement_var = math::max(sq(_params.gravity_noise), sq(0.01f));
|
||||
const ekf_float_t measurement_var = math::max(sq(_params.gravity_noise), sq(0.01f));
|
||||
|
||||
const float upper_accel_limit = CONSTANTS_ONE_G * 1.1f;
|
||||
const float lower_accel_limit = CONSTANTS_ONE_G * 0.9f;
|
||||
@@ -62,8 +62,8 @@ void Ekf::controlGravityFusion(const imuSample &imu)
|
||||
&& !isHorizontalAidingActive();
|
||||
|
||||
// calculate kalman gains and innovation variances
|
||||
Vector3f innovation = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f, -1.f)) - measurement;
|
||||
Vector3f innovation_variance;
|
||||
Vector3<ekf_float_t> innovation = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f, -1.f)) - measurement;
|
||||
Vector3<ekf_float_t> innovation_variance;
|
||||
const auto state_vector = _state.vector();
|
||||
VectorState H;
|
||||
sym::ComputeGravityXyzInnovVarAndHx(state_vector, P, measurement_var, &innovation_variance, &H);
|
||||
@@ -93,14 +93,18 @@ void Ekf::controlGravityFusion(const imuSample &imu)
|
||||
|
||||
} else if (index == 1) {
|
||||
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
|
||||
sym::ComputeGravityYInnovVarAndH(state_vector, P, measurement_var, &_aid_src_gravity.innovation_variance[index], &H);
|
||||
ekf_float_t innov_var;
|
||||
sym::ComputeGravityYInnovVarAndH(state_vector, P, measurement_var, &innov_var, &H);
|
||||
_aid_src_gravity.innovation_variance[index] = innov_var;
|
||||
|
||||
// recalculate innovation using the updated state
|
||||
_aid_src_gravity.innovation[index] = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f, -1.f))(index) - measurement(index);
|
||||
|
||||
} else if (index == 2) {
|
||||
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
|
||||
sym::ComputeGravityZInnovVarAndH(state_vector, P, measurement_var, &_aid_src_gravity.innovation_variance[index], &H);
|
||||
ekf_float_t innov_var;
|
||||
sym::ComputeGravityZInnovVarAndH(state_vector, P, measurement_var, &innov_var, &H);
|
||||
_aid_src_gravity.innovation_variance[index] = innov_var;
|
||||
|
||||
// recalculate innovation using the updated state
|
||||
_aid_src_gravity.innovation[index] = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f, -1.f))(index) - measurement(index);
|
||||
|
||||
@@ -54,16 +54,16 @@
|
||||
bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bool update_all_states)
|
||||
{
|
||||
// XYZ Measurement uncertainty. Need to consider timing errors for fast rotations
|
||||
const float R_MAG = math::max(sq(_params.mag_noise), sq(0.01f));
|
||||
const ekf_float_t R_MAG = math::max(sq(_params.mag_noise), sq(0.01f));
|
||||
|
||||
// calculate intermediate variables used for X axis innovation variance, observation Jacobians and Kalman gains
|
||||
Vector3f mag_innov;
|
||||
Vector3f innov_var;
|
||||
Vector3<ekf_float_t> mag_innov;
|
||||
Vector3<ekf_float_t> innov_var;
|
||||
|
||||
// Observation jacobian and Kalman gain vectors
|
||||
VectorState H;
|
||||
const auto state_vector = _state.vector();
|
||||
sym::ComputeMagInnovInnovVarAndHx(state_vector, P, mag, R_MAG, FLT_EPSILON, &mag_innov, &innov_var, &H);
|
||||
sym::ComputeMagInnovInnovVarAndHx(state_vector, P, Vector3<ekf_float_t>(mag), R_MAG, (ekf_float_t)FLT_EPSILON, &mag_innov, &innov_var, &H);
|
||||
|
||||
// do not use the synthesized measurement for the magnetomter Z component for 3D fusion
|
||||
if (_control_status.flags.synthetic_mag_z) {
|
||||
@@ -127,7 +127,9 @@ bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bo
|
||||
|
||||
} else if (index == 1) {
|
||||
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
|
||||
sym::ComputeMagYInnovVarAndH(state_vector, P, R_MAG, FLT_EPSILON, &aid_src_mag.innovation_variance[index], &H);
|
||||
ekf_float_t innovation_variance;
|
||||
sym::ComputeMagYInnovVarAndH(state_vector, P, (ekf_float_t)R_MAG, (ekf_float_t)FLT_EPSILON, &innovation_variance, &H);
|
||||
aid_src_mag.innovation_variance[index] = innovation_variance;
|
||||
|
||||
// recalculate innovation using the updated state
|
||||
aid_src_mag.innovation[index] = _state.quat_nominal.rotateVectorInverse(_state.mag_I)(index) + _state.mag_B(index) - mag(index);
|
||||
@@ -155,7 +157,9 @@ bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bo
|
||||
}
|
||||
|
||||
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
|
||||
sym::ComputeMagZInnovVarAndH(state_vector, P, R_MAG, FLT_EPSILON, &aid_src_mag.innovation_variance[index], &H);
|
||||
ekf_float_t innovation_variance;
|
||||
sym::ComputeMagZInnovVarAndH(state_vector, P, (ekf_float_t)R_MAG, (ekf_float_t)FLT_EPSILON, &innovation_variance, &H);
|
||||
aid_src_mag.innovation_variance[index] = innovation_variance;
|
||||
|
||||
// recalculate innovation using the updated state
|
||||
aid_src_mag.innovation[index] = _state.quat_nominal.rotateVectorInverse(_state.mag_I)(index) + _state.mag_B(index) - mag(index);
|
||||
@@ -182,8 +186,8 @@ bool Ekf::fuseMag(const Vector3f &mag, estimator_aid_source3d_s &aid_src_mag, bo
|
||||
// zero non-mag Kalman gains if not updating all states
|
||||
|
||||
// copy mag_I and mag_B Kalman gains
|
||||
const Vector3f K_mag_I = Kfusion.slice<State::mag_I.dof, 1>(State::mag_I.idx, 0);
|
||||
const Vector3f K_mag_B = Kfusion.slice<State::mag_B.dof, 1>(State::mag_B.idx, 0);
|
||||
const Vector3<ekf_float_t> K_mag_I = Kfusion.slice<State::mag_I.dof, 1>(State::mag_I.idx, 0);
|
||||
const Vector3<ekf_float_t> K_mag_B = Kfusion.slice<State::mag_B.dof, 1>(State::mag_B.idx, 0);
|
||||
|
||||
// zero all Kalman gains, then restore mag
|
||||
Kfusion.setZero();
|
||||
@@ -246,10 +250,10 @@ bool Ekf::fuseDeclination(float decl_sigma)
|
||||
const float R_DECL = sq(decl_sigma);
|
||||
|
||||
VectorState H;
|
||||
float decl_pred;
|
||||
float innovation_variance;
|
||||
ekf_float_t decl_pred;
|
||||
ekf_float_t innovation_variance;
|
||||
|
||||
sym::ComputeMagDeclinationPredInnovVarAndH(_state.vector(), P, R_DECL, FLT_EPSILON, &decl_pred, &innovation_variance, &H);
|
||||
sym::ComputeMagDeclinationPredInnovVarAndH(_state.vector(), P, (ekf_float_t)R_DECL, (ekf_float_t)FLT_EPSILON, &decl_pred, &innovation_variance, &H);
|
||||
|
||||
const float innovation = wrap_pi(decl_pred - decl_measurement);
|
||||
|
||||
|
||||
@@ -118,8 +118,10 @@ void Ekf::controlMagHeadingFusion(const magSample &mag_sample, const bool common
|
||||
aid_src.time_last_fuse = _time_delayed_us;
|
||||
|
||||
} else {
|
||||
ekf_float_t innov_var;
|
||||
VectorState H_YAW;
|
||||
computeYawInnovVarAndH(aid_src.observation_variance, aid_src.innovation_variance, H_YAW);
|
||||
computeYawInnovVarAndH(aid_src.observation_variance, innov_var, H_YAW);
|
||||
aid_src.innovation_variance = innov_var;
|
||||
|
||||
if ((aid_src.innovation_variance - aid_src.observation_variance) > sq(_params.mag_heading_noise / 2.f)) {
|
||||
// Only fuse mag to constrain the yaw variance to a safe value
|
||||
|
||||
@@ -54,7 +54,7 @@ void Ekf::updateOptFlow(estimator_aid_source2d_s &aid_src)
|
||||
aid_src.timestamp_sample = _flow_sample_delayed.time_us;
|
||||
|
||||
const Vector2f vel_body = predictFlowVelBody();
|
||||
const float range = predictFlowRange();
|
||||
const ekf_float_t range = predictFlowRange();
|
||||
|
||||
// calculate optical LOS rates using optical flow rates that have had the body angular rate contribution removed
|
||||
// correct for gyro bias errors in the data used to do the motion compensation
|
||||
@@ -73,13 +73,13 @@ void Ekf::updateOptFlow(estimator_aid_source2d_s &aid_src)
|
||||
aid_src.innovation[1] = (-vel_body(0) / range) - aid_src.observation[1];
|
||||
|
||||
// calculate the optical flow observation variance
|
||||
const float R_LOS = calcOptFlowMeasVar(_flow_sample_delayed);
|
||||
const ekf_float_t R_LOS = calcOptFlowMeasVar(_flow_sample_delayed);
|
||||
aid_src.observation_variance[0] = R_LOS;
|
||||
aid_src.observation_variance[1] = R_LOS;
|
||||
|
||||
Vector2f innov_var;
|
||||
Vector2<ekf_float_t> innov_var;
|
||||
VectorState H;
|
||||
sym::ComputeFlowXyInnovVarAndHx(_state.vector(), P, range, R_LOS, FLT_EPSILON, &innov_var, &H);
|
||||
sym::ComputeFlowXyInnovVarAndHx(_state.vector(), P, range, R_LOS, (ekf_float_t)FLT_EPSILON, &innov_var, &H);
|
||||
innov_var.copyTo(aid_src.innovation_variance);
|
||||
|
||||
// run the innovation consistency check and record result
|
||||
@@ -88,17 +88,17 @@ void Ekf::updateOptFlow(estimator_aid_source2d_s &aid_src)
|
||||
|
||||
void Ekf::fuseOptFlow()
|
||||
{
|
||||
const float R_LOS = _aid_src_optical_flow.observation_variance[0];
|
||||
const ekf_float_t R_LOS = _aid_src_optical_flow.observation_variance[0];
|
||||
|
||||
// calculate the height above the ground of the optical flow camera. Since earth frame is NED
|
||||
// a positive offset in earth frame leads to a smaller height above the ground.
|
||||
float range = predictFlowRange();
|
||||
ekf_float_t range = predictFlowRange();
|
||||
|
||||
const auto state_vector = _state.vector();
|
||||
|
||||
Vector2f innov_var;
|
||||
Vector2<ekf_float_t> innov_var;
|
||||
VectorState H;
|
||||
sym::ComputeFlowXyInnovVarAndHx(state_vector, P, range, R_LOS, FLT_EPSILON, &innov_var, &H);
|
||||
sym::ComputeFlowXyInnovVarAndHx(state_vector, P, range, R_LOS, (ekf_float_t)FLT_EPSILON, &innov_var, &H);
|
||||
innov_var.copyTo(_aid_src_optical_flow.innovation_variance);
|
||||
|
||||
if ((_aid_src_optical_flow.innovation_variance[0] < R_LOS)
|
||||
@@ -129,7 +129,9 @@ void Ekf::fuseOptFlow()
|
||||
|
||||
} else if (index == 1) {
|
||||
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
|
||||
sym::ComputeFlowYInnovVarAndH(state_vector, P, range, R_LOS, FLT_EPSILON, &_aid_src_optical_flow.innovation_variance[1], &H);
|
||||
ekf_float_t innovation_variance;
|
||||
sym::ComputeFlowYInnovVarAndH(state_vector, P, range, (ekf_float_t)R_LOS, (ekf_float_t)FLT_EPSILON, &innovation_variance, &H);
|
||||
_aid_src_optical_flow.innovation_variance[1] = innovation_variance;
|
||||
|
||||
// recalculate the innovation using the updated state
|
||||
const Vector2f vel_body = predictFlowVelBody();
|
||||
@@ -189,7 +191,8 @@ Vector2f Ekf::predictFlowVelBody()
|
||||
const Vector3f vel_rel_earth = _state.vel + _R_to_earth * vel_rel_imu_body;
|
||||
|
||||
// rotate into body frame
|
||||
return _state.quat_nominal.rotateVectorInverse(vel_rel_earth).xy();
|
||||
Quatf quat_nominal = _state.quat_nominal;
|
||||
return quat_nominal.rotateVectorInverse(vel_rel_earth).xy();
|
||||
}
|
||||
|
||||
// calculate the measurement variance for the optical flow sensor (rad/sec)^2
|
||||
|
||||
@@ -198,7 +198,8 @@ void Ekf::resetHorizontalPositionToLastKnown()
|
||||
_information_events.flags.reset_pos_to_last_known = true;
|
||||
|
||||
// Used when falling back to non-aiding mode of operation
|
||||
resetHorizontalPositionTo(_last_known_pos.xy(), sq(_params.pos_noaid_noise));
|
||||
Vector2f position(_last_known_pos(0), _last_known_pos(1));
|
||||
resetHorizontalPositionTo(position, sq(_params.pos_noaid_noise));
|
||||
}
|
||||
|
||||
void Ekf::resetHorizontalPositionToExternal(const Vector2f &new_horiz_pos, float horiz_accuracy)
|
||||
|
||||
@@ -88,7 +88,7 @@ def generate_python_function(function_name, output_names):
|
||||
output_dir="generated",
|
||||
skip_directory_nesting=True)
|
||||
|
||||
def build_state_struct(state, T="float"):
|
||||
def build_state_struct(state, T="ekf_float_t"):
|
||||
out = "struct StateSample {\n"
|
||||
|
||||
def TypeFromLength(len):
|
||||
@@ -121,7 +121,7 @@ def build_state_struct(state, T="float"):
|
||||
first_field = next(iter(state))
|
||||
|
||||
out += f"\n\tconst matrix::Vector<{T}, {state_size}>& vector() const {{\n" \
|
||||
+ f"\t\treturn *reinterpret_cast<matrix::Vector<{T}, {state_size}>*>(const_cast<float*>(reinterpret_cast<const {T}*>(&{first_field})));\n" \
|
||||
+ f"\t\treturn *reinterpret_cast<matrix::Vector<{T}, {state_size}>*>(const_cast<{T}*>(reinterpret_cast<const {T}*>(&{first_field})));\n" \
|
||||
+ f"\t}};\n\n"
|
||||
|
||||
out += "};\n" # StateSample
|
||||
|
||||
@@ -10,17 +10,17 @@
|
||||
namespace estimator
|
||||
{
|
||||
struct StateSample {
|
||||
matrix::Quaternion<float> quat_nominal{};
|
||||
matrix::Vector3<float> vel{};
|
||||
matrix::Vector3<float> pos{};
|
||||
matrix::Vector3<float> gyro_bias{};
|
||||
matrix::Vector3<float> accel_bias{};
|
||||
matrix::Vector3<float> mag_I{};
|
||||
matrix::Vector3<float> mag_B{};
|
||||
matrix::Vector2<float> wind_vel{};
|
||||
matrix::Quaternion<ekf_float_t> quat_nominal{};
|
||||
matrix::Vector3<ekf_float_t> vel{};
|
||||
matrix::Vector3<ekf_float_t> pos{};
|
||||
matrix::Vector3<ekf_float_t> gyro_bias{};
|
||||
matrix::Vector3<ekf_float_t> accel_bias{};
|
||||
matrix::Vector3<ekf_float_t> mag_I{};
|
||||
matrix::Vector3<ekf_float_t> mag_B{};
|
||||
matrix::Vector2<ekf_float_t> wind_vel{};
|
||||
|
||||
matrix::Vector<float, 24> Data() const {
|
||||
matrix::Vector<float, 24> state;
|
||||
matrix::Vector<ekf_float_t, 24> Data() const {
|
||||
matrix::Vector<ekf_float_t, 24> state;
|
||||
state.slice<4, 1>(0, 0) = quat_nominal;
|
||||
state.slice<3, 1>(4, 0) = vel;
|
||||
state.slice<3, 1>(7, 0) = pos;
|
||||
@@ -32,12 +32,12 @@ struct StateSample {
|
||||
return state;
|
||||
};
|
||||
|
||||
const matrix::Vector<float, 24>& vector() const {
|
||||
return *reinterpret_cast<matrix::Vector<float, 24>*>(const_cast<float*>(reinterpret_cast<const float*>(&quat_nominal)));
|
||||
const matrix::Vector<ekf_float_t, 24>& vector() const {
|
||||
return *reinterpret_cast<matrix::Vector<ekf_float_t, 24>*>(const_cast<ekf_float_t*>(reinterpret_cast<const ekf_float_t*>(&quat_nominal)));
|
||||
};
|
||||
|
||||
};
|
||||
static_assert(sizeof(matrix::Vector<float, 24>) == sizeof(StateSample), "state vector doesn't match StateSample size");
|
||||
static_assert(sizeof(matrix::Vector<ekf_float_t, 24>) == sizeof(StateSample), "state vector doesn't match StateSample size");
|
||||
|
||||
struct IdxDof { unsigned idx; unsigned dof; };
|
||||
namespace State {
|
||||
|
||||
Regular → Executable
@@ -81,11 +81,11 @@ void Ekf::updateSideslip(estimator_aid_source1d_s &sideslip) const
|
||||
// reset flags
|
||||
resetEstimatorAidStatus(sideslip);
|
||||
|
||||
const float R = sq(_params.beta_noise); // observation noise variance
|
||||
const ekf_float_t R = sq(_params.beta_noise); // observation noise variance
|
||||
|
||||
float innov = 0.f;
|
||||
float innov_var = 0.f;
|
||||
sym::ComputeSideslipInnovAndInnovVar(_state.vector(), P, R, FLT_EPSILON, &innov, &innov_var);
|
||||
ekf_float_t innov = 0.f;
|
||||
ekf_float_t innov_var = 0.f;
|
||||
sym::ComputeSideslipInnovAndInnovVar(_state.vector(), P, R, (ekf_float_t)FLT_EPSILON, &innov, &innov_var);
|
||||
|
||||
sideslip.observation = 0.f;
|
||||
sideslip.observation_variance = R;
|
||||
@@ -134,10 +134,10 @@ void Ekf::fuseSideslip(estimator_aid_source1d_s &sideslip)
|
||||
VectorState H; // Observation jacobian
|
||||
VectorState K; // Kalman gain vector
|
||||
|
||||
sym::ComputeSideslipHAndK(_state.vector(), P, sideslip.innovation_variance, FLT_EPSILON, &H, &K);
|
||||
sym::ComputeSideslipHAndK(_state.vector(), P, (ekf_float_t)sideslip.innovation_variance, (ekf_float_t)FLT_EPSILON, &H, &K);
|
||||
|
||||
if (update_wind_only) {
|
||||
const Vector2f K_wind = K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0);
|
||||
const Vector2<ekf_float_t> K_wind = K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0);
|
||||
K.setZero();
|
||||
K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0) = K_wind;
|
||||
}
|
||||
|
||||
@@ -347,16 +347,17 @@ void Ekf::fuseFlowForTerrain(estimator_aid_source2d_s &flow)
|
||||
{
|
||||
flow.fused = true;
|
||||
|
||||
const float R_LOS = flow.observation_variance[0];
|
||||
const ekf_float_t R_LOS = flow.observation_variance[0];
|
||||
|
||||
// calculate the height above the ground of the optical flow camera. Since earth frame is NED
|
||||
// a positive offset in earth frame leads to a smaller height above the ground.
|
||||
float range = predictFlowRange();
|
||||
|
||||
const float state = _terrain_vpos; // linearize both axes using the same state value
|
||||
Vector2f innov_var;
|
||||
float H;
|
||||
sym::TerrEstComputeFlowXyInnovVarAndHx(state, _terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, FLT_EPSILON, &innov_var, &H);
|
||||
const ekf_float_t state = _terrain_vpos; // linearize both axes using the same state value
|
||||
Vector2<ekf_float_t> innov_var;
|
||||
ekf_float_t H;
|
||||
sym::TerrEstComputeFlowXyInnovVarAndHx(state, (ekf_float_t)_terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, (ekf_float_t)FLT_EPSILON, &innov_var, &H);
|
||||
|
||||
innov_var.copyTo(flow.innovation_variance);
|
||||
|
||||
if ((flow.innovation_variance[0] < R_LOS)
|
||||
@@ -385,7 +386,9 @@ void Ekf::fuseFlowForTerrain(estimator_aid_source2d_s &flow)
|
||||
|
||||
} else if (index == 1) {
|
||||
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
|
||||
sym::TerrEstComputeFlowYInnovVarAndH(state, _terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, FLT_EPSILON, &flow.innovation_variance[1], &H);
|
||||
ekf_float_t innovation_variance;
|
||||
sym::TerrEstComputeFlowYInnovVarAndH(state, (ekf_float_t)_terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, (ekf_float_t)FLT_EPSILON, &innovation_variance, &H);
|
||||
flow.innovation_variance[1] = innovation_variance;
|
||||
|
||||
// recalculate the innovation using the updated state
|
||||
const Vector2f vel_body = predictFlowVelBody();
|
||||
|
||||
@@ -40,8 +40,10 @@
|
||||
// update quaternion states and covariances using the yaw innovation and yaw observation variance
|
||||
bool Ekf::fuseYaw(estimator_aid_source1d_s &aid_src_status)
|
||||
{
|
||||
ekf_float_t innov_var;
|
||||
VectorState H_YAW;
|
||||
sym::ComputeYawInnovVarAndH(_state.vector(), P, aid_src_status.observation_variance, &aid_src_status.innovation_variance, &H_YAW);
|
||||
sym::ComputeYawInnovVarAndH(_state.vector(), P, (ekf_float_t)aid_src_status.observation_variance, &innov_var, &H_YAW);
|
||||
aid_src_status.innovation_variance = innov_var;
|
||||
|
||||
return fuseYaw(aid_src_status, H_YAW);
|
||||
}
|
||||
@@ -132,7 +134,7 @@ bool Ekf::fuseYaw(estimator_aid_source1d_s &aid_src_status, const VectorState &H
|
||||
return false;
|
||||
}
|
||||
|
||||
void Ekf::computeYawInnovVarAndH(float variance, float &innovation_variance, VectorState &H_YAW) const
|
||||
void Ekf::computeYawInnovVarAndH(ekf_float_t variance, ekf_float_t &innovation_variance, VectorState &H_YAW) const
|
||||
{
|
||||
sym::ComputeYawInnovVarAndH(_state.vector(), P, variance, &innovation_variance, &H_YAW);
|
||||
}
|
||||
|
||||
@@ -56,9 +56,10 @@ void Ekf::controlZeroInnovationHeadingUpdate()
|
||||
aid_src_status.observation_variance = obs_var;
|
||||
aid_src_status.innovation = 0.f;
|
||||
|
||||
ekf_float_t innov_var;
|
||||
VectorState H_YAW;
|
||||
|
||||
computeYawInnovVarAndH(obs_var, aid_src_status.innovation_variance, H_YAW);
|
||||
computeYawInnovVarAndH(obs_var, innov_var, H_YAW);
|
||||
aid_src_status.innovation_variance = innov_var;
|
||||
|
||||
if (!_control_status.flags.tilt_align || (aid_src_status.innovation_variance - obs_var) > sq(_params.mag_heading_noise)) {
|
||||
// The yaw variance is too large, fuse fake measurement
|
||||
|
||||
@@ -22,6 +22,13 @@ depends on MODULES_EKF2
|
||||
---help---
|
||||
EKF2 support multiple instances and selector.
|
||||
|
||||
menuconfig EKF2_DOUBLE_PRECISION_FLOAT
|
||||
depends on MODULES_EKF2
|
||||
bool "double precision floating point"
|
||||
default n
|
||||
---help---
|
||||
EKF2 double precision floating point.
|
||||
|
||||
menuconfig EKF2_AIRSPEED
|
||||
depends on MODULES_EKF2
|
||||
bool "airspeed fusion support"
|
||||
|
||||
@@ -32,6 +32,8 @@
|
||||
############################################################################
|
||||
add_definitions(-DTEST_DATA_PATH="${CMAKE_CURRENT_SOURCE_DIR}")
|
||||
|
||||
add_compile_options(-Wno-error -Wno-double-promotion) # TODO
|
||||
|
||||
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/..)
|
||||
add_subdirectory(sensor_simulator)
|
||||
add_subdirectory(test_helper)
|
||||
@@ -45,18 +47,18 @@ px4_add_unit_gtest(SRC test_EKF_gyroscope.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_fusionLogic.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
|
||||
px4_add_unit_gtest(SRC test_EKF_gps.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
|
||||
px4_add_unit_gtest(SRC test_EKF_gps_yaw.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_gnss_yaw_generated.cpp LINKLIBS ecl_EKF ecl_test_helper)
|
||||
#px4_add_unit_gtest(SRC test_EKF_gnss_yaw_generated.cpp LINKLIBS ecl_EKF ecl_test_helper)
|
||||
px4_add_unit_gtest(SRC test_EKF_height_fusion.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
|
||||
px4_add_unit_gtest(SRC test_EKF_imuSampling.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_initialization.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_mag.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_mag_declination_generated.cpp LINKLIBS ecl_EKF ecl_test_helper)
|
||||
#px4_add_unit_gtest(SRC test_EKF_mag_declination_generated.cpp LINKLIBS ecl_EKF ecl_test_helper)
|
||||
px4_add_unit_gtest(SRC test_EKF_measurementSampling.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_ringbuffer.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_terrain_estimator.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
|
||||
px4_add_unit_gtest(SRC test_EKF_utils.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_withReplayData.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_yaw_estimator.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
|
||||
px4_add_unit_gtest(SRC test_EKF_yaw_fusion_generated.cpp LINKLIBS ecl_EKF ecl_test_helper)
|
||||
#px4_add_unit_gtest(SRC test_EKF_yaw_fusion_generated.cpp LINKLIBS ecl_EKF ecl_test_helper)
|
||||
px4_add_unit_gtest(SRC test_SensorRangeFinder.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
px4_add_unit_gtest(SRC test_EKF_drag_fusion.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
|
||||
|
||||
Reference in New Issue
Block a user