A Code for Unscented Kalman Filtering on Manifolds (UKF-M)
This work provides a practical tool for state estimation in robotics and related fields, though it is incremental as it extends prior work on UKF on Lie groups.
The paper tackles the problem of Unscented Kalman Filtering on manifolds by introducing a versatile and simple-to-implement method, resulting in open-source Python and Matlab frameworks (UKF-M) with tutorials and robotics examples for fast prototyping and benchmarking.
The present paper introduces a novel methodology for Unscented Kalman Filtering (UKF) on manifolds that extends previous work by the authors on UKF on Lie groups. Beyond filtering performance, the main interests of the approach are its versatility, as the method applies to numerous state estimation problems, and its simplicity of implementation for practitioners not being necessarily familiar with manifolds and Lie groups. We have developed the method on two independent open-source Python and Matlab frameworks we call UKF-M, for quickly implementing and testing the approach. The online repositories contain tutorials, documentation, and various relevant robotics examples that the user can readily reproduce and then adapt, for fast prototyping and benchmarking. The code is available at https://github.com/CAOR-MINES-ParisTech/ukfm.