Throughout the code the state sqrt covariance S and innovation covariance Sy are maintained as upper triangular cholesky factors of those covariance matrices. The original paper defines P=S*S', so S should be lower triangular. The functions in the paper reflect this. In the code implementation, the sqrt covariance matrices are upper triangular, but the algorithm expects them to be lower triangular.
This bug was likely missed because the incorrect version of the filter is able to converge for some systems where all the states are observed, and the test case is set up such that all states are observed.
To fix the bug, a couple things needed to be changed:
all instances of rankUpdate() needed to be changed to use the lower triangular cholesky factor,
In the unscented transform, when S is found via QR decomposition, we need to take the transpose because R is upper triangular,
P() and SetP() functions need to be modified to be P=S*S' instead of P=S'*S, and P.llt().matrixL() instead of P.llt().matrixU() respectively.
Each part of the algorithm has also had the comments changed to clarify exactly which equation from the paper it implements.
Co-authored-by: Tyler Veness <calcmogul@gmail.com>
We now use a wrapper (wpi::print) to catch exceptions since we can't patch
std::print() to not throw when we ultimately migrate to it.
fmtlib and std format/print throw the same exceptions and always have. We previously patched fmt::print() to not throw a write failure exception, but we can't do that for std::print(); wpi::print() is the migration plan.
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.
Inconsistent names were found using the following regular expressions.
* `rg "TEST(_F|_P)?\(\w+,\s+\w+Test\)"`
* `rg "TEST(_F|_P)?\(\w+,\s+Test\w+\)"`
* `rg "TEST(_F|_P)?\(\w+Tests,\s+\w+\)"`
Fixes#3495.
* 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()
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.
Pose and state estimators can filter latency-compensated global measurements and fuse them with state-space drivetrain model information to estimate robot position. They are drop-in replacements for the existing odometry classes.
Co-authored-by: Declan Freeman-Gleason <declanfreemangleason@gmail.com>
Co-authored-by: Prateek Machiraju <prateek.machiraju@gmail.com>
Co-authored-by: Claudius Tewari <cttewari@gmail.com>
Co-authored-by: Matt <matthew.morley.ca@gmail.com>
