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Real-Time Dynamic Laser SLAM Algorithm Combining Object-Level Geometric Features and Semantic Information
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Fengchong Lan1, 2, Xiaoqiang Tian1, 2, Jiqing Chen1, 2, Yuxiang Che1, 2, Yunjiao Zhou1, 2
Automotive Engineering | 2024, 46(11) : 2028 - 2038
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Automotive Engineering | 2024, 46(11): 2028-2038
Feature Topic:Key Technologies on Intelligent and Connected Vehicles
Real-Time Dynamic Laser SLAM Algorithm Combining Object-Level Geometric Features and Semantic Information
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Fengchong Lan1, 2, Xiaoqiang Tian1, 2, Jiqing Chen1, 2, Yuxiang Che1, 2, Yunjiao Zhou1, 2
Affiliations
  • 1. School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640
  • 2. South China University of Technology,Guangdong Provincial Key Laboratory of Automotive Engineering,Guangzhou 510640
Published: 2024-11-25 doi: 10.19562/j.chinasae.qcgc.2024.11.009
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In view of the problems of the existing laser SLAM algorithm in dynamic scenes,which has poor robustness and the positioning and mapping accuracy is easily disturbed by dynamic objects,a real-time dynamic laser SLAM algorithm called Object-SuMa that combines object-level geometric feature and semantic information is proposed. Firstly,through processes such as ground filtering,object segmentation and pose size calculation,object-level geometric features are generated and represented as texture and used to correct semantic segmentation errors within the object. Then,in the odometry stage,the IOU calculation of the oriented bounding box is decomposed,and object-level geometric weighting and semantic weighting are introduced based on the bounding box IOU and semantic segmentation results to reduce mismatching and dynamic point matching. In addition,the graphics rendering pipeline is used to build a parallel computing process,and the computational complexity and time consuming are reduced by two-step optimization of ground point registration and non-ground point registration. Finally,tests on the KITTI odometry data set show that compared with SuMa++,the Object-SuMa algorithm has improved the relative pose accuracy by 15% and reduced the average time of ICP by 17%,which improves the positioning accuracy and robustness of laser SLAM in dynamic scenarios.

laser SLAM  /  dynamic environment  /  object-level geometric feature  /  semantic information  /  parallel computing
Fengchong Lan, Xiaoqiang Tian, Jiqing Chen, Yuxiang Che, Yunjiao Zhou. Real-Time Dynamic Laser SLAM Algorithm Combining Object-Level Geometric Features and Semantic Information[J]. Automotive Engineering, 2024 , 46 (11) : 2028 -2038 . DOI: 10.19562/j.chinasae.qcgc.2024.11.009
Year 2024 volume 46 Issue 11
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.11.009
  • Receive Date:2024-02-26
  • Online Date:2025-07-21
  • Published:2024-11-25
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  • Received:2024-02-26
  • Revised:2024-05-30
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    1. School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640
    2. South China University of Technology,Guangdong Provincial Key Laboratory of Automotive Engineering,Guangzhou 510640
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https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2024.11.009
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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