MCFilter: feature filter based on motion-correlation for LiDAR SLAM
Robotic Intelligence and Automation
ISSN: 2754-6969
Article publication date: 8 December 2023
Issue publication date: 29 March 2024
Abstract
Purpose
This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.
Design/methodology/approach
Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.
Findings
Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.
Originality/value
In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.
Keywords
Acknowledgements
This work is partially supported by the German Research Foundation and National Natural Science Foundation of China in project Crossmodal Learning under contract Sonderforschungsbereich Transregio 169, the Hamburg Landesforschungsfӧrderungsprojekt Cross, National Natural Science Foundation of China (61773083); Horizon2020 RISE project STEP2DYNA (691154); National Natural Science Foundation of China (62206168, 62276048, 52375035).
Citation
Sun, H., Tang, S., Qi, X., Ma, Z. and Gao, J. (2024), "MCFilter: feature filter based on motion-correlation for LiDAR SLAM", Robotic Intelligence and Automation, Vol. 44 No. 1, pp. 68-83. https://doi.org/10.1108/RIA-07-2023-0086
Publisher
:Emerald Publishing Limited
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