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This work develops a learning-based contact estimator for legged robots thatbypasses the need for physical sensors and takes multi-modal proprioceptivesensory data as input. Unlike vision-based state estimators, proprioceptivestate estimators are agnostic to perceptually degraded situations such as darkor foggy scenes. While some robots are equipped with dedicated physical sensorsto detect necessary contact data for state estimation, some robots do not havededicated contact sensors, and the addition of such sensors is non-trivialwithout redesigning the hardware. The trained network can estimate contactevents on different terrains. The experiments show that a contact-aidedinvariant extended Kalman filter can generate accurate odometry trajectoriescompared to a state-of-the-art visual SLAM system, enabling robustproprioceptive odometry.

Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events