Title: ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle Show Chinese title

Title: ROLO-SLAM：在不平坦地形中基于地面车辆的旋转优化激光雷达仅SLAM

Abstract: 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. Show Chinese abstract

Abstract: 基于LiDAR的SLAM被认为是提供粗糙环境中定位引导的一种有效方法。 然而，现成的基于LiDAR的SLAM方法在通过不平坦地形时，特别是在与垂直方向相关的组件中，会受到显著的姿态估计漂移的影响。 这种缺陷通常会导致全局地图明显失真。 本文提出了一种基于LiDAR的SLAM方法，以提高在粗糙地形中地面车辆的姿态估计精度，该方法称为旋转优化LiDAR-only（ROLO）SLAM。 该方法利用前向位置预测来粗略消除连续扫描的位置差异，从而实现前端位置和方向的独立和精确确定。 此外，我们采用一种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一种球面对齐引导的旋转配准，以估计车辆的旋转。 通过引入几何对齐，我们将运动约束纳入优化公式，以增强LiDAR平移的快速有效估计。 随后，我们提取几个关键帧来构建子地图，并利用当前扫描到子地图的对齐来进行精确的姿态估计。 同时，建立一个全局尺度因子图，以帮助减少累积误差。 在各种场景中，进行了多种实验来评估我们的方法。 结果表明，ROLO-SLAM在地面车辆的姿态估计方面表现出色，并优于现有的最先进的LiDAR SLAM框架。