Abstract: 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.

Key words: quadruped robot, woodland localization, LiDAR, leg odometer, filter