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[wpimath] Use Odometry for internal state in Pose Estimation (#4668)

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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>
This commit is contained in:
Jordan McMichael
2022-12-02 11:36:10 -05:00
committed by GitHub
parent 68dba92630
commit e22d8cc343
35 changed files with 2288 additions and 1884 deletions
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wpilibcExamples/src/main/cpp/examples/DifferentialDrivePoseEstimator/include/Drivetrain.h
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@@ -79,12 +79,12 @@ class Drivetrain {
// Gains are for example purposes only - must be determined for your own
// robot!
frc::DifferentialDrivePoseEstimator m_poseEstimator{
m_kinematics,
m_gyro.GetRotation2d(),
units::meter_t{m_leftEncoder.GetDistance()},
units::meter_t{m_rightEncoder.GetDistance()},
frc::Pose2d{},
{0.01, 0.01, 0.01, 0.01, 0.01},
{0.1, 0.1, 0.1},
{0.01, 0.01, 0.01},
{0.1, 0.1, 0.1}};
// Gains are for example purposes only - must be determined for your own