Mobile robot localization method based on point-line feature visual-inertial SLAM algorithm
Abstract
Purpose
The purpose of this study is to address the low localization accuracy and frequent tracking failures of traditional visual SLAM methods in low-light and weak-texture situations, and we propose a mobile robot visual-inertial localization method based on the improved point-line features VINS-mono algorithm.
Design/methodology/approach
First, the line feature information is introduced into VINS-mono. Subsequently, the EDlines line feature extraction algorithm is optimized with a short line merging strategy and a dynamic length suppression strategy to reduce redundant short lines and fragmented segments. In the back-end sliding window optimization, line feature reprojection errors are incorporated, and Huber kernel functions are added to the inertial measurement unit residuals, point-line feature residuals and loop closure constraints to reduce the impact of outliers on the optimization results.
Findings
Comparison and verification experiments are carried out on the EuRoC MAV Data set and real weakly textured environment. In the real low-light and weak-texture scenarios, the improved mobile robot localization system achieves over 40% higher accuracy compared to VINS-mono.
Originality/value
The main contribution of this study is to propose a new visual-inertial SLAM method combining point-line features, which can achieve good localization effect in low-light and weak-texture scenes, with higher accuracy and robustness.
Keywords
Acknowledgements
Thanks to all the undersigned authors who contribute to this manuscript. This work is supported by Class III Peak Discipline of Shanghai–Materials Science and Engineering (High-Energy Beam Intelligent Processing and Green Manufacturing).
Citation
Xu, J., Fang, Y., Gao, W., Liu, X., Shi, J. and Yang, H. (2024), "Mobile robot localization method based on point-line feature visual-inertial SLAM algorithm", Industrial Robot, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IR-08-2024-0381
Publisher
:Emerald Publishing Limited
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