Abstract:

Currently， most LiDAR-SLAM systems utilized front-end odometry to estimate the initial pose and back-end optimization to refine the pose， but they lacked batch back-end optimization approaches. To address these issues， a comprehensive LiDAR-SLAM system was proposed based on explicit geometric features. This system employed agglomerative hierarchical clustering for plane feature point cloud segmentation and employed local curvature computation to filter linear feature points. Furthermore， the initial pose estimation of LiDAR motion was achieved through registering point cloud features and submap features. A local state optimization method was utilized based on linear and planar primitives， where linear and planar factors were merged within a factor graph model. By minimizing residuals between linear-to-linear and plane-to-plane associations， joint batch optimization of pose， linear， and planar parameters was achieved. Experimental results demonstrate that the proposed SLAM system achieves high precision localization and map construction in various scenarios， meeting real-time SLAM requirements.

Key words: simultaneous localization and mapping（SLAM）, LiDAR odometry, feature extraction, nonlinear optimization

摘要：

目前多数LiDAR-SLAM系统采用前端里程计估计初始位姿和后端优化位姿的方法，缺少批量的后端优化方案。针对此问题，提出了一个完整的基于显式几何特征的激光雷达同时定位与建图（SLAM）系统。采用凝聚层次聚类方法实现平面特征点云平面分割并通过计算点云的局部曲率值筛选直线特征点；通过配准点云特征和特征子地图实现激光雷达运动的初始位姿估计；采用基于直线和平面基元的局部状态优化方法，基于因子图模型融合了直线因子和平面因子，通过最小化直线到直线和平面到平面的残差，实现了位姿、直线和平面参数的联合批量优化。实验结果表明，所提SLAM系统在其他场景下也能实现较高精度的定位和地图构建，满足SLAM的实时性要求。

关键词: 同时定位与建图, 激光雷达里程计, 特征提取, 非线性优化