A sensor fusion-based optimization method for indoor localization

Guohao Huang; Haibin Huang; Weijiang Xiao; Yanyang Dang; Jiangwei Xie; Xingyu Gao; Yang Huang

doi:10.1117/12.3026603

1 April 2024 A sensor fusion-based optimization method for indoor localization

Guohao Huang, Haibin Huang, Weijiang Xiao, Yanyang Dang, Jiangwei Xie, Xingyu Gao, Yang Huang

Proceedings Volume 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023); 130820P (2024) https://doi.org/10.1117/12.3026603
Event: 2023 4th International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology (MEMAT 2023), 2023, Guilin, China

Abstract

Indoor differential-driven mobile robots play a crucial role in industrial applications such as intelligent inspection and smart transportation. In scenarios with uneven road friction and/or sharp turn, challenges remain for accurate robot localization in indoor environment. When using encoders of the wheel odometry as the sole basis, the robot will lead to significant odometry drift and inaccurate localization. To optimize the wheel odometry data, this paper presents an internal and external combined sensor fusion method for laser simultaneous localization and mapping (SLAM) system. Experimental results demonstrate that the proposed method significantly reduces cumulative localization error by comparing with method using solely wheel odometry. It also effectively corrects an average translational odometry error caused by inherent wheel odometry drift.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guohao Huang, Haibin Huang, Weijiang Xiao, Yanyang Dang, Jiangwei Xie, Xingyu Gao, and Yang Huang "A sensor fusion-based optimization method for indoor localization", Proc. SPIE 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023), 130820P (1 April 2024); https://doi.org/10.1117/12.3026603

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KEYWORDS

Sensors

Motion models

LIDAR

Mobile robots

Sensor fusion

Covariance matrices

Environmental sensing

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