触覚でナビゲート：幾何情報と地形分類を融合した触覚モンテカルロ位置推定
Navigating by Touch: Haptic Monte Carlo Localization via Geometric Sensing and Terrain Classification
脚式ロボットの足裏力と関節・IMU情報のみから地形クラス確率と足接地点の幾何情報を融合し、事前地図内で自己位置を推定する手法を提案。ANYmal四脚ロボットで1.2km以上走行し誤差20cm未満を達成した。
著者: Russell Buchanan, Jakub Bednarek, Marco Camurri, Michał R. Nowicki, Krzysztof Walas, Maurice Fallon
分類: cs.RO
原文アブストラクト
Legged robot navigation in extreme environments can hinder the use of cameras and laser scanners due to darkness, air obfuscation or sensor damage. In these conditions, proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain class, to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain class probability with the geometric information of the foot contact points. Results are demonstrated showing this approach operating online and onboard a ANYmal B300 quadruped robot traversing a series of terrain courses with different geometries and terrain types over more than 1.2km. The method keeps the localization error below 20cm using only the information coming from the feet, IMU, and joints of the quadruped.
