The Joseph form of the error covariance update equation is more
numerically stable when the Kalman gain isn't optimal. Numerical
instability and filter divergence can occur if the user goes long time
periods between updates and the error covariance becomes ill-conditioned
(the ratio between the largest and smallest eigenvalue gets too large).
The existing implementation will produce a cost of NaN if a tolerance of
infinity is entered, but the limit approaches zero. Being able to
specify that a state has no cost is useful, so this change adds support for
that.
When trying to set the tolerance of a ProfiledPID, it fails if you don't give it a velocity value. It was missing a conversion from double to the appropiate unit.
It would crash in C++ if the global measurement was sooner than all the
snapshots.
Align Java with the changes and better document computation approach.
It was using the continuous B matrix to compute the feedback gain
instead of the discrete B matrix.
Tests were added for the matrix constructor overloads.
These classes are useful for storing previous robot positions to use in conjunction with the upcoming pose estimators.
Co-authored-by: Prateek Machiraju <prateek.machiraju@gmail.com>
Co-authored-by: Tyler Veness <calcmogul@gmail.com>
Co-authored-by: cttew <cttewari@gmail.com>
This avoids stack-use-after-scope bugs in code like the following when
the original argument goes out of scope:
```cpp
frc2::Command* RobotContainer::GetAutonomousCommand() {
// Create a voltage constraint to ensure we don't accelerate too fast
frc::DifferentialDriveVoltageConstraint autoVoltageConstraint(
frc::SimpleMotorFeedforward<units::meters>(
DriveConstants::ks, DriveConstants::kv, DriveConstants::ka),
DriveConstants::kDriveKinematics, 10_V);
```
This also makes the Gradle build work with JDK 17.
The extra JVM args in gradle.properties works around a bug with spotless
and JDK 17: https://github.com/diffplug/spotless/issues/834
PMD.CloseResource was ignored because it's almost always a false
positive, and there are many of them.
The template argument order for UnscentedTransform was reversed to match
all the other UKF classes. Since UnscentedTransform is intended as a
class for internal use only, this shouldn't cause much breakage.
* Replace Matrix<> with Vector<> where vectors are explicitly intended.
I found these via `rg "Eigen::Matrix<double, \w+, 1>"`.
* Pass all Eigen matrices by const reference. I found these via `rg
"\(Eigen"` on main (the initializer list constructors make more false
positives).
* Replace MakeMatrix() and operator<< usage with initializer list
constructors. I found these via `rg MakeMatrix` and `rg "<<"`
respectively.
* Deprecate MakeMatrix()
Internal headers are no longer allowed as of
https://gitlab.com/libeigen/eigen/-/merge_requests/631. Based on
benchmarking I conducted in that thread, there doesn't seem to be a
performance penalty for including the full headers anymore.
