ROLO-SLAM：地面车辆在非平坦地形中的旋转优化纯LiDAR SLAM
ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle
AI总结：
提出ROLO-SLAM，通过前向位置预测、体素内球面旋转配准、运动约束平移估计、子图对齐与全局因子图，提升地面车辆在非平坦地形中的纯LiDAR定位建图精度。
摘要：
基于LiDAR的SLAM被认为是在粗糙环境中提供定位引导的一种有效方法。然而，当进入非平坦地形时，现成的基于LiDAR的SLAM方法会出现显著的位姿估计漂移，尤其是与垂直方向相关的分量。这一缺陷通常会导致全局地图明显失真。本文提出一种基于LiDAR的SLAM方法，用于提高地面车辆在粗糙地形中的位姿估计精度，该方法称为Rotation-Optimized LiDAR-Only（ROLO）SLAM。该方法利用前向位置预测粗略消除连续扫描之间的位置差异，从而能够在前端分别且准确地确定位置和朝向。此外，我们采用支持并行的空间体素化进行对应匹配。我们在每个体素内开发了球面对齐引导的旋转配准，以估计车辆旋转。通过结合几何对齐，我们将运动约束引入优化公式，以增强LiDAR平移的快速有效估计。随后，我们提取若干关键帧构建子图，并利用当前扫描到子图的对齐进行精确位姿估计。同时，建立全局尺度因子图以帮助减少累积误差。在各种场景中，我们进行了多种实验来评估所提方法。结果表明，ROLO-SLAM在地面车辆位姿估计方面表现优异，并优于现有最先进的LiDAR SLAM框架。
英文摘要：
LiDAR-based SLAM is recognized as one effective method to offer localization guidance in rough environments. However, off-the-shelf LiDAR-based SLAM methods suffer from significant pose estimation drifts, particularly components relevant to the vertical direction, when passing to uneven terrains. This deficiency typically leads to a conspicuously distorted global map. In this article, a LiDAR-based SLAM method is presented to improve the accuracy of pose estimations for ground vehicles in rough terrains, which is termed Rotation-Optimized LiDAR-Only (ROLO) SLAM. The method exploits a forward location prediction to coarsely eliminate the location difference of consecutive scans, thereby enabling separate and accurate determination of the location and orientation at the front-end. Furthermore, we adopt a parallel-capable spatial voxelization for correspondence-matching. We develop a spherical alignment-guided rotation registration within each voxel to estimate the rotation of vehicle. By incorporating geometric alignment, we introduce the motion constraint into the optimization formulation to enhance the rapid and effective estimation of LiDAR's translation. Subsequently, we extract several keyframes to construct the submap and exploit an alignment from the current scan to the submap for precise pose estimation. Meanwhile, a global-scale factor graph is established to aid in the reduction of cumulative errors. In various scenes, diverse experiments have been conducted to evaluate our method. The results demonstrate that ROLO-SLAM excels in pose estimation of ground vehicles and outperforms existing state-of-the-art LiDAR SLAM frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
