激光雷达
计算机科学
束流调整
图形
弹道
人工智能
过程(计算)
移动地图
计算机视觉
机器人
直线(几何图形)
数据挖掘
遥感
地理
数学
图像(数学)
理论计算机科学
物理
几何学
天文
点云
操作系统
作者
Bin Shi,Wanbiao Lin,Wei Ouyang,Chaopeng Shen,Siyang Sun,Yan Sun,Lei Sun
标识
DOI:10.20944/preprints202407.2274.v1
摘要
Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrated that the proposed method achieves accurate trajectory estimation and consistent mapping.
科研通智能强力驱动
Strongly Powered by AbleSci AI