Invariant Smoothing for Localization: Including the IMU Biases - CAO et robotique (CAOR)
Communication Dans Un Congrès Année : 2024

Invariant Smoothing for Localization: Including the IMU Biases

Résumé

In this article we investigate smoothing (i.e., optimisation-based) estimation techniques for robot localization using an IMU aided by other localization sensors. We more particularly focus on Invariant Smoothing (IS), a variant based on the use of nontrivial Lie groups from robotics. We study the recently introduced Two Frames Group (TFG), and prove it can fit into the framework of Invariant Smoothing in order to better take into account the IMU biases, as compared to the state-of-the-art in robotics. Experiments based on the KITTI dataset show the proposed framework compares favorably to the state-of-the-art smoothing methods in terms of robustness in some challenging situations.
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Dates et versions

hal-04212340 , version 1 (20-09-2023)
hal-04212340 , version 2 (22-09-2023)
hal-04212340 , version 3 (03-09-2024)

Identifiants

Citer

Paul Chauchat, Silvère Bonnabel, Axel Barrau. Invariant Smoothing for Localization: Including the IMU Biases. 2024 IEEE 63th Annual Conference on Decision and Control (CDC), Dec 2024, Milan, Italy. ⟨hal-04212340v3⟩
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