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Legged robot navigation in extreme environments can hinder the use of camerasand laser scanners due to darkness, air obfuscation or sensor damage. In theseconditions, proprioceptive sensing will continue to work reliably. In thispaper, we propose a purely proprioceptive localization algorithm which fusesinformation from both geometry and terrain class, to localize a legged robotwithin a prior map. First, a terrain classifier computes the probability that afoot has stepped on a particular terrain class from sensed foot forces. Then, aMonte Carlo-based estimator fuses this terrain class probability with thegeometric information of the foot contact points. Results are demonstratedshowing this approach operating online and onboard a ANYmal B300 quadrupedrobot traversing a series of terrain courses with different geometries andterrain types over more than 1.2km. The method keeps the localization errorbelow 20cm using only the information coming from the feet, IMU, and joints ofthe quadruped.

Navigating by Touch: Haptic Monte Carlo Localization via Geometric Sensing and Terrain Classification