  We present an open-source Visual-Inertial-Leg Odometry (VILO) state
estimation solution, Cerberus, for legged robots that estimates position
precisely 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 also perform online kinematic parameter
calibration and contact outlier rejection to substantially reduce position
drift. Hardware experiments in various indoor and outdoor environments validate
that calibrating kinematic parameters within the Cerberus can reduce estimation
drift to lower than 1% during long distance high speed locomotion. Our drift
results are better than 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 is experiencing large impacts and camera occlusion. The
implementation of the state estimator, along with the datasets used to compute
our results, are available at https://github.com/ShuoYangRobotics/Cerberus.
