Vehicle-Borne Multi-Sensor Temporal-Spatial Pose Globalization via Cross-Domain Data Association

doi:10.1109/TITS.2023.3292396

	Vehicle-Borne Multi-Sensor Temporal-Spatial Pose Globalization via Cross-Domain Data Association
	Gao, Xiang1,2,3 ; Tao, Dongdong 4; Liu, Yuqian 5; Xie, Zexiao 4; Shen, Shuhan1,2,3
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2023-07-17
页码	14
通讯作者	Xie, Zexiao(xiezexiao@ouc.edu.cn) ; Shen, Shuhan(shshen@nlpr.ia.ac.cn)
摘要	Large-scale urban scene 3D mapping has urgent demands and wide applications in many areas, where sensor pose globalization remains its fundamental problem and critical step. As the street-view images and vehicle-borne Light Detection And Ranging (LiDAR) points contain complementary advantages in urban scene 3D mapping, it is desirable to make the most of both to facilitate this task. Most existing methods make strong assumptions of strict synchronization, and even further, exact calibration between the vehicle-borne cameras and LiDARs, which are hard to guarantee in practice. To deal with this, we propose a novel pipeline for vehicle-borne camera and LiDAR temporal and spatial pose globalization with the guidance of Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU), where both of the assumptions on strict synchronization and exact calibration are loosened. Specifically, the global poses of both cameras and LiDARs are first initialized by leveraging GNSS/IMU signals and multi-sensor pre-calibrations, and then refined by a global optimization scheme. To perform the global pose optimization, image-based, LiDAR-based, and cross-domain data association and constraint construction are conducted. Among them, the cross-domain ones, which are achieved by LiDAR point projection, image feature back-projection, and spatial point association, provide key clues for associating these two kinds of data with significant differences. Comprehensive experiments on both of a self-collected and the KITTI Odometry datasets demonstrate the effectiveness of our proposed method on multi-sensor pose globalization for large-scale urban scene 3D mapping.
关键词	Index Terms-Urban scene 3D mapping image and LiDAR pose globalization cross-domain data association GNSS/IMU
DOI	10.1109/TITS.2023.3292396
关键词[WOS]	REGISTRATION ; ODOMETRY ; FEATURES
收录类别	SCI
语种	英语
资助项目	National Science Foundation of China[62003319] ; National Science Foundation of China[U22B2055] ; National Science Foundation of China[42076192] ; Shandong Provincial Natural Science Foundation[ZR2020QF075] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27000000]
项目资助者	National Science Foundation of China ; Shandong Provincial Natural Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:001035827500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53847
专题	中国科学院工业视觉智能装备工程实验室
通讯作者	Xie, Zexiao; Shen, Shuhan
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.CASIA SenseTime Res Grp, Beijing 100190, Peoples R China 4.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China 5.SenseTime Res, Hangzhou 311215, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Gao, Xiang,Tao, Dongdong,Liu, Yuqian,et al. Vehicle-Borne Multi-Sensor Temporal-Spatial Pose Globalization via Cross-Domain Data Association[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2023:14.
APA	Gao, Xiang,Tao, Dongdong,Liu, Yuqian,Xie, Zexiao,&Shen, Shuhan.(2023).Vehicle-Borne Multi-Sensor Temporal-Spatial Pose Globalization via Cross-Domain Data Association.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,14.
MLA	Gao, Xiang,et al."Vehicle-Borne Multi-Sensor Temporal-Spatial Pose Globalization via Cross-Domain Data Association".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023):14.