基于因子图优化的光伏电站场景激光雷达SLAM方法

Lidar SLAM Method for Photovoltaic Power Stations Based on Factor Graph Optimization

光伏电站具有场地尺度大、场景中结构稀少、阵列排布光伏组件形成狭长过道的环境特点。针对搭载2D激光雷达的巡检机器人使用simultaneous localization and mapping (SLAM)算法在光伏电站场景中进行定位和地图构建时出现位姿估计不准确、地图显示不完整的问题,以Cartographer算法为框架,提出一种基于因子图优化的前端优化策略。通过预积分处理构建惯性测量单元(IMU)因子,联合激光雷达数据扫描匹配后位姿因子,共同作为约束加入因子图中进行优化,获得更准确的估计位姿,并将此位姿嵌入原始算法进行地图构建。搭建模拟光伏电站环境和模拟狭长过道环境,对主流滤波算法、Cartographer算法和改进后算法进行对比实验,结果表明改进后算法所构建的地图尺寸精度更高,整体描述更准确。

Photovoltaic power stations have the environmental characteristics of largescale sites,sparse structural elements,and narrow corridors caused by arrays of photovoltaic modules.In response to the issues of inaccurate pose estimation and incomplete mapping encountered while using the simultaneous localization and mapping(SLAM)algorithm with a twodimensional LiDARequipped inspection robot for location and mapping in photovoltaic power stations,we propose an algorithm by adopting the Cartographer algorithm as a framework and incorporating a frontend optimization strategy based on factor graph optimization.Herein,we construct inertial measurement unit(IMU)factors through preintegration processing and match pose factors from LiDAR data scanning.Then,we jointly add them as constraints to the factor graph for optimization to obtain more accurate estimated poses and embed these poses into the original algorithm for map construction.Additionally,Experiments were conducted in a simulated photovoltaic power station and a simulated narrow corridor with mainstream filtering,Cartographer,and improved algorithms.Results reveal that our improved algorithm generates maps with a higher dimensional accuracy and a more accurate overall description.