Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile Locomotion

We present an open-source Visual-Inertial-Leg Odometry (VILO) state estimation solution for legged robots, called Cerberus, which precisely estimates position on various terrains in real-time using a set of standard sensors, including stereo cameras, IMU, joint encoders, and contact sensors. In addition to estimating robot states, we perform online kinematic parameter calibration and outlier rejection to substantially reduce position drift. Hardware experiments in various indoor and outdoor environments validate that online calibration of kinematic parameters can reduce estimation drift to less than 1% during long-distance, high-speed locomotion. Our drift results are better than those of any other state estimation method using the same set of sensors reported in the literature. Moreover, our state estimator performs well even when the robot experiences large impacts and camera occlusion. The implementation of the state estimator, along with the datasets used to compute our results, is available at https://github.com/ShuoYangRobotics/Cerberus.