Invariant Smoothing for Localization: Including the IMU Biases

TL;DR

This paper enhances robot localization accuracy by integrating IMU biases into Invariant Smoothing using Lie groups, demonstrating improved robustness over existing methods on real-world datasets.

Contribution

It introduces a novel approach to incorporate IMU biases into Invariant Smoothing via the Two Frames Group, advancing state-of-the-art localization techniques.

Findings

Improved robustness in challenging localization scenarios

Effective integration of IMU biases into the smoothing framework

Outperforms existing methods on KITTI dataset

Abstract

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.