Paul Riseborough dbfe8c0242 EKF: remove approximation in mag fusion innovation variance calculation
The covariance was not being updated with the observation from one axis before the innovation variance was calculated for the next axis. This results in greater weighting on measurements for subsequent axes.
2016-06-08 11:58:33 +10:00
2016-04-25 14:39:11 -04:00
2016-02-17 17:38:21 -08:00
2016-02-17 17:34:28 -08:00
2016-02-17 17:38:21 -08:00
2016-05-10 17:59:01 +02:00
2016-04-16 21:46:50 -04:00
2016-05-19 18:14:33 +10:00
2016-06-02 16:29:55 +01:00
2015-10-26 15:41:25 +01:00

ECL

Very lightweight Estimation & Control Library.

Build Status

This library solves the estimation & control problems of a number of robots and drones. It accepts GPS, vision and inertial sensor inputs. It is extremely lightweight and efficient and yet has the rugged field-proven performance.

The library is currently BSD licensed, but might move to Apache 2.0.

Building EKF Library

Prerequisites:

Ubuntu:

sudo apt-get install libeigen3-dev

Mac

brew install eigen

By following the steps mentioned below you can create a shared library which can be included in projects using -l flag of gcc:

mkdir Build/
cd Build/
cmake ../EKF
make

Alternatively, just run:

./build.sh
Description
a mirror of official PX4-Autopilot
Readme BSD-3-Clause 587 MiB
Languages
C++ 51.2%
C 38.5%
CMake 4.7%
Python 3.9%
Shell 1.3%
Other 0.1%