This effectively replaces the Unscented Kalman Filter used for Pose Estimation with the Odometry model, and uses a recalculable Kalman gain matrix to update pose estimations whenever a vision measurement is added.
Notably, this change removes the need for the confusing generics used in Java, and the C++ implementation got quite a bit less complex as well.
Co-authored-by: Tyler Veness <calcmogul@gmail.com>
* Use explicit this capture required by C++20
* Use C++20 span
* Replace wpi::numbers with std::numbers
* Fix C++20 clang-tidy warning false positive in fmt
* Remove ciso646 include since C++20 removed that header
* Fix global-buffer-overflow asan warnings in ntcore tests
* Add DIOSetProxy constructor to HAL
* Upgrade MSVC compiler to 2022
* Bump native-utils to 2023.2.7 (changes to std=c++20)
Co-authored-by: Peter Johnson <johnson.peter@gmail.com>
Now, implicit narrowing conversions are only used with wpi::Now(). This
also fixes clang-tidy warnings about C-style casts. For example:
```
== clang-tidy /__w/allwpilib/allwpilib/wpilibNewCommands/src/main/native/include/frc2/command/SwerveControllerCommand.inc ==
/__w/allwpilib/allwpilib/wpilibNewCommands/src/main/native/include/frc2/command/SwerveControllerCommand.inc:95:18: warning: C-style casts are discouraged; use static_cast/const_cast/reinterpret_cast [google-readability-casting]
auto curTime = units::second_t(m_timer.Get());
^
```
In that case at least, the cast was removed entirely since Get() already
returns a units::second_t.
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).
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.
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.
This also fixes a member function name inconsistency between languages
and adds missing documentation to C++'s KalmanFilterLatencyCompensator.
Fixes#3229.
A lot of these are breaking changes. frc::Timer was replaced with the
contents of frc2::Timer. The others were in-place argument changes or
removing deprecated non-unit overloads.
The wpimath APIs use std::array, which doesn't do size checking. Passing
an array with the wrong size can result in uninitialized elements
instead of a compilation error.
This is a breaking change but is worthwhile to avoid hard-to-debug errors.
frc::NormalizeAngle(), units::math::NormalizeAngle(), and
frc::GetModulusError() were replaced with frc::InputModulus() and
frc::AngleModulus().
They were placed in wpimath/src/main/native/include/frc/MathUtil.h for
C++ and wpimath/src/main/java/edu/wpi/first/wpiutil/math/MathUtil.java
for Java.
