Navigating by Touch: Haptic Monte Carlo Localization via Geometric Sensing and Terrain Classification
This enables robust navigation for legged robots in conditions like darkness or sensor damage, though it is incremental as it builds on existing proprioceptive and Monte Carlo techniques.
The paper tackles legged robot localization in extreme environments where cameras and lasers fail, by fusing geometric and terrain classification data from proprioceptive sensors. The method achieved online localization with errors below 20cm over 1.2km of varied terrain using only onboard sensors.
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.