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Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process

文献类型：期刊论文

作者Shen, Zhihao1; Liang, Huawei2,3; Lin, Linglong2,4; Wang, Zhiling2,3; Huang, Weixin1,2; Yu, Jie2,4
刊名REMOTE SENSING
出版日期2021-08-01
卷号13
关键词autonomous vehicles LiDAR ground segmentation convolution real-time
DOI10.3390/rs13163239
通讯作者Lin, Linglong(linll@iim.ac.cn)
英文摘要LiDAR occupies a vital position in self-driving as the advanced detection technology enables autonomous vehicles (AVs) to obtain much environmental information. Ground segmentation for LiDAR point cloud is a crucial procedure to ensure AVs' driving safety. However, some current algorithms suffer from embarrassments such as unavailability on complex terrains, excessive time and memory usage, and additional pre-training requirements. The Jump-Convolution-Process (JCP) is proposed to solve these issues. JCP converts the segmentation problem of the 3D point cloud into the smoothing problem of the 2D image and takes little time to improve the segmentation effect significantly. First, the point cloud marked by an improved local feature extraction algorithm is projected onto an RGB image. Then, the pixel value is initialized with the points' label and continuously updated according to image convolution. Finally, a jump operation is introduced in the convolution process to perform calculations only on the low-confidence points filtered by the credibility propagation algorithm, reducing the time cost. Experiments on three datasets show that our approach has a better segmentation accuracy and terrain adaptability than those of the three existing methods. Meanwhile, the average time for the proposed method to deal with one scan data of 64-beam and 128-beam LiDAR is only 8.61 ms and 15.62 ms, which fully meets the AVs' requirement for real-time performance.
资助项目National Key Research and Development Program of China[2020AAA0108103] ; National Key Research and Development Program of China[2016YFD0701401] ; National Key Research and Development Program of China[2017YFD0700303] ; National Key Research and Development Program of China[2018YFD0700602] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2017488] ; Key Supported Project in the Thirteenth Five-year Plan of Hefei Institutes of Physical Science, Chinese Academy of Sciences[KP-2019-16] ; Natural Science Foundation of Anhui Province[1808085QF213] ; Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000689946500001
出版者MDPI
资助机构National Key Research and Development Program of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; Key Supported Project in the Thirteenth Five-year Plan of Hefei Institutes of Physical Science, Chinese Academy of Sciences ; Natural Science Foundation of Anhui Province ; Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/125129]  
专题中国科学院合肥物质科学研究院
通讯作者Lin, Linglong
作者单位1.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
3.Anhui Engn Lab Intelligent Driving Technol & Appl, Hefei 230031, Peoples R China
4.Chinese Acad Sci, Innovat Res Inst Robot & Intelligent Mfg, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Shen, Zhihao,Liang, Huawei,Lin, Linglong,et al. Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process[J]. REMOTE SENSING,2021,13.
APA Shen, Zhihao,Liang, Huawei,Lin, Linglong,Wang, Zhiling,Huang, Weixin,&Yu, Jie.(2021).Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process.REMOTE SENSING,13.
MLA Shen, Zhihao,et al."Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process".REMOTE SENSING 13(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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