  Continuous robot operation in extreme scenarios such as underground mines or
sewers is difficult because exteroceptive sensors may fail due to fog,
darkness, dirt or malfunction. So as to enable autonomous navigation in these
kinds of situations, we have developed a type of proprioceptive localization
which exploits the foot contacts made by a quadruped robot to localize against
a prior map of an environment, without the help of any camera or LIDAR sensor.
The proposed method enables the robot to accurately re-localize itself after
making a sequence of contact events over a terrain feature. The method is based
on Sequential Monte Carlo and can support both 2.5D and 3D prior map
representations. We have tested the approach online and onboard the ANYmal
quadruped robot in two different scenarios: the traversal of a custom built
wooden terrain course and a wall probing and following task. In both scenarios,
the robot is able to effectively achieve a localization match and to execute a
desired pre-planned path. The method keeps the localization error down to 10cm
on feature rich terrain by only using its feet, kinematic and inertial sensing.
