Research on method of simultaneous localization and mapping of coal mine inspection robot
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摘要: 针对煤矿井下无GPS环境下巡检机器人自主定位问题，研究了基于激光雷达的同步定位与地图构建方法。首先建立激光雷达观测模型和里程计预测模型，将机器人定位和地图构建的实际问题转换为概率数学模型的逻辑推理问题。同时采用自适应蒙特卡罗定位算法进行机器人实时位姿估计，提出了根据粒子权重（地图的匹配度）进行重采样的方法,以去除权重小的粒子，实现了用较少、较好粒子精确表达机器人位姿的后验概率分布，满足机器人利用传感器在栅格地图上实时定位的需求。通过对Fast-SLAM算法进行优化，减少了粒子数量，缓解了粒子耗散,提高了地图构建的精确性。实验结果表明，基于激光雷达的同步定位与地图构建方法有效解决了巡检机器人实时位姿估计和环境地图构建的问题，结合自适应蒙特卡罗定位算法和优化Fast-SLAM算法提高了机器人定位的自适应性和地图构建的精确性。Abstract: In view of problem of autonomous location of inspection robot without GPS in underground coal mine, a method of simultaneous localization and mapping based on lidar was studied. Firstly, the observation model of lidar and prediction model of odometer are established, and the actual problems of robot localization and mapping are transformed into the logical reasoning problems of probabilistic mathematical model. At the same time, the adaptive Monte Carlo localization algorithm is used to estimate the real-time pose of the robot,the resampling method based on particle weight(maps matching degree) is proposed to remove particles with small weight, accurate representation of posterior probability distribution of robot posture with fewer and better particles is realized, requirement of using sensors to realize the real-time positioning of robots on raster maps is met. Fast-SLAM algorithm is optimized to reduce the number of particles, and mitigate particle dissipation,so as to improve accuracy of mapping. The experimental results show that the method effectively solves the problem of real-time pose estimation and environment mapping of inspection robot, and improves the self-adaptability of robot localization and accuracy of mapping combining with adaptive Monte Carlo localization algorithm and optimized Fast-SLAM algorithm.
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Keywords:
- coal mine inspection robot /
- pose estimation /
- simultaneous localization /
- mapping /
- scan-matching for map /
- lidar
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期刊类型引用(7)
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其他类型引用(4)
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