IEKF_SE2Example.cpp File Reference

A left invariant Extended Kalman Filter example using a Lie group odometry as the prediction stage on SE(2) and. More...

#include <gtsam/geometry/Pose2.h>
#include <gtsam/navigation/InvariantEKF.h>
#include <iostream>

Include dependency graph for IEKF_SE2Example.cpp:

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Functions
Vector2	h_gps (const Pose2 &X, OptionalJacobian< 2, 3 > H={})
int	main ()

Detailed Description

A left invariant Extended Kalman Filter example using a Lie group odometry as the prediction stage on SE(2) and.

This example uses the templated InvariantEKF class to estimate the state of an object using odometry / GPS measurements The prediction stage of the InvariantEKF uses a Lie Group element to propagate the stage in a discrete InvariantEKF. For most cases, U = exp(u^ * dt) if u is a control vector that is constant over the interval dt. However, if u is not constant over dt, other approaches are needed to find the value of U. This approach simply takes a Lie group element U, which can be found in various different ways.

This data was compared to a left invariant EKF on SE(2) using identical measurements and noise from the source of the InEKF plugin https://inekf.readthedocs.io/en/latest/ Based on the paper "An Introduction to the Invariant Extended Kalman Filter" by Easton R. Potokar, Randal W. Beard, and Joshua G. Mangelson

Date

Apr 25, 2025

Authors

Scott Baker, Matt Kielo, Frank Dellaert

Definition in file IEKF_SE2Example.cpp.

Function Documentation

h_gps()

Vector2 h_gps	(	const Pose2 &	X,
		OptionalJacobian< 2, 3 >	H = {}
	)

Definition at line 45 of file IEKF_SE2Example.cpp.

main()

int main

(

)

Definition at line 51 of file IEKF_SE2Example.cpp.