Invariant Smoothing for Localization: Including the IMU Biases

Fuente: arXiv
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Main Authors: Chauchat, Paul, Bonnabel, Silvère, Barrau, Axel
Format: Preprint
Published: 2023
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author Chauchat, Paul
Bonnabel, Silvère
Barrau, Axel
author_facet Chauchat, Paul
Bonnabel, Silvère
Barrau, Axel
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2309_13903
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Invariant Smoothing for Localization: Including the IMU Biases
Chauchat, Paul
Bonnabel, Silvère
Barrau, Axel
Robotics
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.
title Invariant Smoothing for Localization: Including the IMU Biases
topic Robotics
url https://arxiv.org/abs/2309.13903