OR-LIM: Observability-aware robust LiDAR-inertial-mapping under high dynamic sensor motion

作     者：Cong, Yangzi Chen, Chi Yang, Bisheng Zhong, Ruofei Sun, Shangzhe Xu, Yuhang Yan, Zhengfei Zou, Xianghong Tu, Zhigang 

作者机构：Shandong Univ Sch Space Sci & Phys Weihai Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China Minist Educ PRC Engn Res Ctr Space Time Data Capturing & Smart App Wuhan Peoples R China Wuhan Univ Inst Geospatial Intelligence Wuhan Peoples R China Capital Normal Univ Coll Resource Environm & Tourism Beijing Peoples R China 

出 版 物：《ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING》 (ISPRS J. Photogramm. Remote Sens.)

年 卷 期：2024年第218卷

页      面：610-627页

核心收录：

学科分类：1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0705[理学-地理学] 0816[工学-测绘科学与技术] 

基　　金：National Key RESEARCH and Development Program [2022YFB3904101] National Natural Science Foundation of China [42071451, 42130105, U22A20568] Key RESEARCH and Development Program of Hubei Province [2023BAB146] Research Program of State Grid Corporation of China [5500-202316189A-1-1-ZN] Fundamental Research Funds for the Central Universities, LIESMARS Special Research Funding European Union [871149, GZC20231482] 

主　　题：LiDAR remote sensing 3D mapping system Dynamic motion model Data acquisition 

摘      要：Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D data. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. In this paper, we present OR-LIM, a novel observability-aware LiDAR-inertial-mapping system. Essentially, it combines a robust real-time LiDAR-inertial-odometry (LIO) module with an efficient surfel-map-smoothing (SMS) module that seamlessly optimizes the sensor poses and scene geometry at the same time. To improve robustness, the planar surfels are hierarchically generated and grown from point cloud maps to provide reliable correspondences for fixed-lag optimization. Moreover, the normals of surfels are analyzed for the observability evaluation of each frame. To maintain global consistency, a factor graph is utilized integrating the information from IMU propagation, LIO as well as the SMS. The system is extensively tested on the datasets collected by a low-cost multi-beam LiDAR (MBL) mounted on a rotating platform. The experiments with various settings of sensor motion, conducted on complex multi-story buildings and large-scale outdoor scenes, demonstrate the superior performance of our system over multiple state-of-the-art methods. The improvement of point accuracy reaches 3.39-13.6 % with an average 8.71 % outdoor and correspondingly 1.89-15.88 % with 9.09 % indoor, with reference to the collected Terrestrial Laser Scanning (TLS) map.