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Conference papers

A Code for Unscented Kalman Filtering on Manifolds (UKF-M)

Abstract : 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.
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Contributor : Martin Brossard Connect in order to contact the contributor
Submitted on : Tuesday, March 10, 2020 - 9:32:58 AM
Last modification on : Wednesday, November 17, 2021 - 12:33:07 PM
Long-term archiving on: : Thursday, June 11, 2020 - 2:44:27 PM


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  • HAL Id : hal-02463553, version 2
  • ARXIV : 2002.00878


Martin Brossard, Axel Barrau, Silvere Bonnabel. A Code for Unscented Kalman Filtering on Manifolds (UKF-M). International Conference on Robotics and Automation (ICRA), May 2020, Paris, France. ⟨hal-02463553v2⟩



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