Equivariant IMU Preintegration With Biases: A Galilean Group Approach

被引:0
|
作者
Delama, Giulio [1 ]
Fornasier, Alessandro [1 ]
Mahony, Robert [2 ]
Weiss, Stephan [1 ]
机构
[1] Univ Klagenfurt, Control Networked Syst Grp, A-9020 Klagenfurt, Austria
[2] Australian Natl Univ, Syst Theory & Robot Lab, Acton 0200, Australia
来源
IEEE ROBOTICS AND AUTOMATION LETTERS | 2025年 / 10卷 / 01期
基金
欧盟地平线“2020”;
关键词
Lie groups; Navigation; Manifolds; Filtering theory; Vectors; Location awareness; Algebra; Accuracy; Libraries; Kalman filters; Localization; sensor fusion; SLAM; SYSTEMS;
D O I
10.1109/LRA.2024.3511424
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
This letter proposes a new approach for Inertial Measurement Unit (IMU) preintegration, a fundamental building block that can be leveraged in different optimization-based Inertial Navigation System (INS) localization solutions. Inspired by recent advances in equivariant theory applied to biased INSs, we derive a discrete-time formulation of the IMU preintegration on Gal(3)gal(3) , the left-trivialization of the tangent group of the Galilean group Gal(3) . We define a novel preintegration error that geometrically couples the navigation states and the bias leading to lower linearization error. Our method improves in consistency compared to existing preintegration approaches which treat IMU biases as a separate state-space. Extensive validation against state-of-the-art methods, both in simulation and with real-world IMU data, implementation in the Lie++ library, and open-source code are provided.
引用
收藏
页码:724 / 731
页数:8
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