- voting is now at a central place instead of duplicated within the
estimators
-> this also means that estimators that did not do voting so far,
now have voting, like ekf2
- estimators requiring more than that can still subscribe to the raw
sensors
- allows sensors_combined to be 3 times smaller
- reduces logger, memcpy (cache) & RAM overhead
- all modules requiring only 1 or 2 sensor values now automatically get
the voted result
- this also adds voting to baro
Decreases the message size from 780 to 280 bytes.
In particular, all modules using sensor_combined must use the integral now.
The sensor value can easily be reconstructed by dividing with dt.
Voters now need to be moved into sensors module, because error count and
priority is removed from the topic.
Any module that requires additional data from a sensor can subscribe to
the raw sensor topics.
At two places, values are set to zero instead of subscribing to the raw
sensors (with the assumption that no one reads them):
- mavlink mavlink_highres_imu_t::abs_pressure
- sdlog2: sensor temperatures
These are not really used. differential_pressure is just copied from the
topic with the same name.
for sdlog2 we assume no one needs the diff pressure fields and set it to 0.
We plan to switch to the new logger soon anyway.
Drawbacks of the previous method: when writing to the SD card, there are
high delays in the write() call of several 100ms, every now and then. The
frequency and length of these events depend on:
- SD card
- used logger bandwidth
- bandwidth of gps data (RTCM)
Since the whole gps thread was blocked during this period, it lead to
gps timeouts and lost module.
What we do now is: publish an orb topic with queuing. This makes it async
and the logger takes care of buffering. This means it's best to:
- use high logger rate
- use large logger buffer
- reduce logger bandwith by disabling unused topics
This was horribly wrong. Matrix is first cast into a matrix of size NxM (which is supposed to be the size of the result - NOT the starting point) so the transpose result becomes garbage. Instead make "Me" an MxN matrix as the original. Took me a whole evening to figure out this problem. Now my Kalman filter finally returns good results.
* Use variance to control publishing for LPE.
* Don't stop publishing if we have gps/ baro.
* LPE tuning and cleanup.
* Added bias saturation to LPE.
* Added vector enabled low pass filter block.
* Added rk4 integration and pub lowpass to LPE.
* Fix std::abs issue on mac/ reset lowpass on state reset.
* Don't estimate gyro bias when rotating at high speed att_est_q.
* Lowered low pass on position to 5 Hz for LPE.
* Streamline state space update for LPE.
* Added health flags to est2 log.
* Revert to old tuning, more conservative, less faults.
* Formatting.
* Fix for fault message on LPE.
* Added subscription throttling to LPE.
* Formatting.
Beat Küng