3D Vehicle Detection With RSU LiDAR for Autonomous Mine
文献类型：期刊论文
| 作者 | Wang, Guojun1; Wu, Jian1,2; Xu, Tong2; Tian, Bin3,4
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| 刊名 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
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| 出版日期 | 2021 |
| 卷号 | 70期号:1页码:344-355 |
| 关键词 | Laser radar Three-dimensional displays Filtering Vehicle detection Detectors Roads Feature extraction Background filtering 3D object detection deep learning roadside LiDAR point cloud |
| ISSN号 | 0018-9545 |
| DOI | 10.1109/TVT.2020.3048985 |
| 通讯作者 | Tian, Bin(bin.tian@ia.ac.cn) |
| 英文摘要 | With the development of intelligent and connected vehicles, RSU (roadside unit) sensors are playing an increasingly important role for environment perception. For vehicle detection in autonomous mine, lack of diversity data on RSU LiDAR limits the application of deep learning based methods. To solve this issue, a voxel-based background filtering module is introduced into 3D object detectors for vehicle detection with RSU LiDAR in mine environments. The proposed background filtering method models average height and the number of points for each voxel as Gaussian distribution to generate a background table. To address the impact of the false negative points of the background filtering module, we also propose a multivariate Gaussian loss to model bounding box uncertainty. The predicted covariances between variates help to learn the relationship between the missed parts and the visible ones. Besides, a background filtering based data augmentation method for vehicle detection is also proposed in this paper. Three RSU LiDAR datasets with different terrains in the BaoLi mine area are used for comprehensive experiment evaluations. Experiments show that the proposed background filtering module and multivariate Gaussian loss can significantly improve the generalization ability and performance of several state-of-the-art 3D detectors on different terrain data. Moreover, most background voxels are filtered out, the inference time of the 3D detectors is about 2x faster. Besides, the effectiveness of the proposed data augmentation method is also demonstrated. |
| 资助项目 | Key-Area Research and Development Program of Guangdong Province[2020B090921003] ; National Natural Science Foundation of China[61503380] ; National Natural Science Foundation of China[61773381] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) |
| WOS研究方向 | Engineering ; Telecommunications ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000617762400026 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43259]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Tian, Bin |
| 作者单位 | 1.Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130022, Peoples R China 2.Waytous Inc, Beijing 100080, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Qingdao Acad Intelligent Ind, Qingdao 266109, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Guojun,Wu, Jian,Xu, Tong,et al. 3D Vehicle Detection With RSU LiDAR for Autonomous Mine[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2021,70(1):344-355. |
| APA | Wang, Guojun,Wu, Jian,Xu, Tong,&Tian, Bin.(2021).3D Vehicle Detection With RSU LiDAR for Autonomous Mine.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,70(1),344-355. |
| MLA | Wang, Guojun,et al."3D Vehicle Detection With RSU LiDAR for Autonomous Mine".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 70.1(2021):344-355. |
入库方式： OAI收割
来源：自动化研究所
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