  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.
