首页|基于因子图优化的光伏电站场景激光雷达SLAM方法

基于因子图优化的光伏电站场景激光雷达SLAM方法

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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.
Lidar SLAM Method for Photovoltaic Power Stations Based on Factor Graph Optimization
Photovoltaic power stations have the environmental characteristics of large-scale 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 two-dimensional LiDAR-equipped 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 front-end 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.

lidarphotovoltaic power stationpre-integrationfactor graph optimizationsensor fusion

汪方斌、曹锟、龚雪、朱达荣、汪萍

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安徽建筑大学机械与电气工程学院,安徽 合肥 230601

安徽建筑大学建筑机械故障诊断与预警技术重点实验室,安徽 合肥 230601

激光雷达 光伏电站场景 预积分 因子图优化 传感器融合

安徽省自然科学基金安徽省教育厅高校自然科学重点项目安徽省教育厅高校研究生科学研究项目安徽省教育厅高校协同创新项目安徽省住房城乡建设科学技术计划项目安徽省住房城乡建设科学技术计划项目安徽省住房城乡建设科学技术计划项目安徽省高等学校科学研究项目

2008085UD09KJ2020A0487YJS20210512GXXT-2021-0102022-YF0162022-YF0652023-YF0502022AH040044

2024

10.3788/LOP232263

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子：1.153
ISSN：1006-4125
年,卷(期)：2024.61(14)
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