面向林地环境的四足机器人自主定位方法

Autonomous localization of quadruped robot in woodland environment

林地是四足机器人野外作业的典型场景,其树木多且间距小,对四足机器人快速导航的定位频率和精度提出了更高的要求。采用腿部里程计定位可以获得较高的更新频率,但林地地面松软、凹凸不平等会引起足端打滑,使其精度降低;而激光雷达定位,虽然林地环境特征丰富,但其存在一定匹配误差且更新频率低,也难以满足快速导航要求。针对此问题,提出了一种适用于林地环境的自主定位方法,采用腿部里程计去除激光雷达点云畸变,并分别提取林地地面和树干特征进行匹配,提高激光雷达定位精度;在激光雷达2次定位之间采用中值和窗口滤波融合腿部里程计的插值数据,提高定位频率。在林地实验中,四足机器人行走110 m,最终偏移为0.09 m,在设定路线导航下最终定位值与期望值相差0.2 m,定位频率500 Hz,四足机器人能准确顺利地完成导航任务。

Woodland is a typical scenario for quadruped robots to operate in the field,where there are many trees and small spacing,which places higher demands on the positioning frequency and accuracy of quadruped robots for rapid navigation.Using leg odometry for positioning can achieve higher update rates,but soft and uneven woodland terrain can cause slippage at the foot ends,resulting in lower accuracy.On the other hand,although woodland environments are rich in features for LiDAR positioning,there are certain matching errors and low update rates,which also make it difficult to meet the requirements of rapid navigation.To address this issue,an autonomous positioning method suitable for woodland environments is proposed,which uses leg odometry to remove LiDAR point cloud distortion and extracts woodland ground and trunk features for matching to improve LiDAR positioning accuracy.Between two LiDAR positioning sessions,median and window filtering are used to fuse interpolated data from the leg odometry to increase the positioning frequency.In woodland experiments,the quadruped robot walked 110 meters with a final deviation of 0.09 m.Under the set route navigation,the final positioning value differs from the expected value by 0.2 m,with a positioning frequency of 500 Hz.The quadruped robot can accurately and successfully complete the navigation task.