Mao Wandeng, Jiang Liang, Liu Shanfeng, Xi Shengzhe, Bao Hua
State Grid Henan Electric Power Research Institute, Zhengzhou, Henan, China.
State Grid Henan Electric Power Company, Zhengzhou, Henan, China.
PLoS One. 2025 Jul 31;20(7):e0328169. doi: 10.1371/journal.pone.0328169. eCollection 2025.
Inspection mobile robots equipped with 3D LiDAR sensors are now widely used in substations and other critical circumstances. However, the application of traditional LiDAR sensors is restricted in large-scale environments. Prolonged operation poses the risk of sensor degradation, while the presence of dynamic objects disrupts the stability of the constructed map, consequently impacting the accuracy of robot localization. To address these challenges, we propose a 3D LiDAR-based long-term localization and map maintenance system, enabling autonomous deployment and operation of inspection robots. The whole system is composed of three key subsystems: a hierarchical SLAM system, a global localization system, and a map maintenance system. The SLAM subsystem includes Local Map Representation, LiDAR Odometry, Global Map Formulation and Optimization, and Dense Map Generation. Specifically, we construct an efficient map representation that voxelizes only the occupied space and computes local geometry within each voxel. The design of LiDAR Odometry ensures high consistency with this map representation mechanism. Then, to address drift errors, we formulate the global map as a graph of local submaps that undergo global optimization. Furthermore, we utilize marching cubes to generate a mesh model of the map. Our system outperforms the state-of-the-art LiDAR odometry method, LOAM, reducing average absolute position error by 30 % and 38 % on two public datasets. The comparative evaluation highlights the system's superior accuracy and robustness, and demonstrates its high SLAM ranking in real-world scenarios. For global localization, we propose a novel ScanContext-ICP method, which integrates our improved ScanContext method, termed ScanContext++, for place recognition and global pose initialization. The Iterative Closest Point (ICP) algorithm is then employed for precise point cloud alignment and pose refinement, enabling the recovery of the robot's position on the offline map when localization is lost. Finally, the map maintenance system tracks environmental changes, distinguishing stable features from dynamic ones. The system assigns higher weight to stable voxels, thereby improving localization accuracy. Furthermore, our time distribution mechanism refines map updates by filtering unstable points through temporal and segment-level analysis, which further enhances map maintenance. We conduct extensive experiments on public datasets to validate our system. The experimental results demonstrate that our system is effective and can be deployed on inspection mobile robots.
配备3D激光雷达传感器的巡检移动机器人目前广泛应用于变电站等关键场所。然而，传统激光雷达传感器在大规模环境中的应用受到限制。长时间运行存在传感器性能下降的风险，而动态物体的存在会破坏所构建地图的稳定性，进而影响机器人定位的准确性。为应对这些挑战，我们提出了一种基于3D激光雷达的长期定位与地图维护系统，实现巡检机器人的自主部署与运行。整个系统由三个关键子系统组成：分层同步定位与地图构建（SLAM）系统、全局定位系统和地图维护系统。SLAM子系统包括局部地图表示、激光雷达里程计、全局地图构建与优化以及密集地图生成。具体而言，我们构建了一种高效的地图表示方法，仅对占据空间进行体素化处理，并在每个体素内计算局部几何信息。激光雷达里程计的设计确保了与这种地图表示机制高度一致。然后，为解决漂移误差问题，我们将全局地图构建为局部子地图的图结构，并进行全局优化。此外，我们利用移动立方体算法生成地图的网格模型。我们的系统性能优于当前最先进的激光雷达里程计方法LOAM，在两个公开数据集上平均绝对位置误差降低了分别30%和38%。对比评估突出了该系统卓越的准确性和鲁棒性，并证明了其在实际场景中的高SLAM排名。对于全局定位，我们提出了一种新颖的ScanContext-ICP方法，该方法集成了我们改进的用于地点识别和全局姿态初始化的ScanContext方法，即ScanContext++。然后采用迭代最近点（ICP）算法进行精确点云对齐和姿态优化，以便在定位丢失时恢复机器人在离线地图上的位置。最后，地图维护系统跟踪环境变化，区分稳定特征和动态特征。该系统为稳定体素赋予更高权重，从而提高定位准确性。此外，我们的时间分布机制通过时间和片段级分析过滤不稳定点来优化地图更新，进一步增强了地图维护。我们在公开数据集上进行了广泛实验以验证我们的系统。实验结果表明我们的系统是有效的，并且可以部署在巡检移动机器人上。
