Paper Info
Title
A Lightweight And Accurate Localization Algorithm Using Multiple Inertial Measurement Units
Abstract
This paper proposes a novel inertial-aided localization approach by fusing information from multiple inertial measurement units (IMUs) and exteroceptive sensors. IMU is a low-cost motion sensor which provides measurements on angular velocity and gravity compensated linear acceleration of a moving platform, and widely used in modern localization systems. To date, most existing inertial-aided localization methods exploit only one single IMU. While the single-IMU localization yields acceptable accuracy and robustness for different use cases, the overall performance can be further improved by using multiple IMUs. To this end, we propose a lightweight and accurate algorithm for fusing measurements from multiple IMUs and exteroceptive sensors, which is able to obtain noticeable performance gain without incurring additional computational cost. To achieve this, we first probabilistically map measurements from all IMUs onto a virtual IMU. This step is performed by stochastic estimation with least-square estimators and probabilistic marginalization of inter-IMU rotational accelerations. Subsequently, the propagation model for both state and error state of the virtual IMU is also derived, which enables the use of the classical filter-based or optimization-based sensor fusion algorithms for localization. Finally, results from both simulation and real-world tests are provided, which demonstrate that the proposed algorithm outperforms competing algorithms by noticeable margins.
Year
DOI
Venue
2020
10.1109/LRA.2020.2969146
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Sensor Fusion, Localization, SLAM
Journal
5
Issue
ISSN
Citations 
2
2377-3766
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Ming Zhang100.68
Xiangyu Xu200.34
Yiming Chen318722.75
Mingyang Li427017.60