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Article

LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments

1
Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China
2
Beijing Institute of Remote Sensing Information, Beijing 100011, China
3
Aviation University Air Force, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jiju Poovvancheri, Zhengxin Zhang, Liqiang Zhang and Dong Chen
Remote Sens. 2021, 13(18), 3651; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183651
Received: 20 July 2021 / Revised: 25 August 2021 / Accepted: 1 September 2021 / Published: 13 September 2021
(This article belongs to the Special Issue Advances in Deep Learning Based 3D Scene Understanding from LiDAR)
Facing the realistic demands of the application environment of robots, the application of simultaneous localisation and mapping (SLAM) has gradually moved from static environments to complex dynamic environments, while traditional SLAM methods usually result in pose estimation deviations caused by errors in data association due to the interference of dynamic elements in the environment. This problem is effectively solved in the present study by proposing a SLAM approach based on light detection and ranging (LiDAR) under semantic constraints in dynamic environments. Four main modules are used for the projection of point cloud data, semantic segmentation, dynamic element screening, and semantic map construction. A LiDAR point cloud semantic segmentation network SANet based on a spatial attention mechanism is proposed, which significantly improves the real-time performance and accuracy of point cloud semantic segmentation. A dynamic element selection algorithm is designed and used with prior knowledge to significantly reduce the pose estimation deviations caused by SLAM dynamic elements. The results of experiments conducted on the public datasets SemanticKITTI, KITTI, and SemanticPOSS show that the accuracy and robustness of the proposed approach are significantly improved. View Full-Text
Keywords: dynamic environments; semantic segmentation; semantic constraints; screening of environmental elements; semantic map dynamic environments; semantic segmentation; semantic constraints; screening of environmental elements; semantic map
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MDPI and ACS Style

Wang, W.; You, X.; Zhang, X.; Chen, L.; Zhang, L.; Liu, X. LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments. Remote Sens. 2021, 13, 3651. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183651

AMA Style

Wang W, You X, Zhang X, Chen L, Zhang L, Liu X. LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments. Remote Sensing. 2021; 13(18):3651. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183651

Chicago/Turabian Style

Wang, Weiqi, Xiong You, Xin Zhang, Lingyu Chen, Lantian Zhang, and Xu Liu. 2021. "LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments" Remote Sensing 13, no. 18: 3651. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183651

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