CAOM: Change-aware online 3D mapping with heterogeneous multi-beam and push-broom LiDAR point clouds

Cong, Yangzi; Chen, Chi; Yang, Bisheng; Liang, Fuyuan; Ma, Ruiqi; Zhang, Fei

doi:10.1016/j.isprsjprs.2022.11.017

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…Visual SLAM based on cameras (Campos et al, 2021;Forster et al, 2017) is prone to failure in conditions of low light, rain, and snow. Conversely, LiDAR SLAM (Cong et al, 2022(Cong et al, , 2023Zhang & Singh, 2014) relies on long-distance observation and stronger robustness in conditions such as poor lighting and harsh environments. It can not only provide high-precision 6 DOF state estimation, but also obtain high-resolution environmental perception maps.…”

Section: Introductionmentioning

confidence: 99%

Pmlio: Panoramic Tightly-Coupled Multi-Lidar-Inertial Odometry and Mapping

Xu,

Chen,

Wang

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The limited field of view (FoV) of single LiDAR poses challenges for robots to achieve comprehensive environmental perception. Incorporating multiple LiDAR sensors can effectively broaden the FoV of robots, providing abundant measurements to facilitate simultaneous localization and mapping (SLAM). In this paper, we propose a panoramic tightly-coupled multi-LiDAR-inertial odometry and mapping framework, which fully leverages the properties of solid-state LiDAR and spinning LiDAR. The key of the proposed framework lies in the effective completion of multi-LiDAR spatial-temporal fusion. Additionally, we employ the iterated extended Kalman filter to achieve tightly-coupled inertial odometry and mapping with IMU data. PMLIO showcases competitive performance on multiple scenarios data, compared with state-of-the-art single LiDAR-inertial SLAM algorithms, and reaches a noteworthy improvement of 27.1% and 12.9% in max and median of absolute pose error (APE) respectively.

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