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

文献类型：期刊论文

作者Gao, Xiang2,3,4; Tao, Dongdong1; Liu, Yuqian5; Xie, Zexiao1; Shen, Shuhan2,3,4
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2023-07-17
页码14
ISSN号1524-9050
关键词Index Terms-Urban scene 3D mapping image and LiDAR pose globalization cross-domain data association GNSS/IMU
DOI10.1109/TITS.2023.3292396
通讯作者Xie, Zexiao(xiezexiao@ouc.edu.cn) ; Shen, Shuhan(shshen@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词REGISTRATION ; ODOMETRY ; FEATURES
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]
WOS研究方向Engineering ; Transportation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001035827500001
资助机构National Science Foundation of China ; Shandong Provincial Natural Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/53846]  
专题中科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.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.CASIA SenseTime Res Grp, Beijing 100190, Peoples R China
5.SenseTime Res, Hangzhou 311215, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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