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

TL;DR

Cerberus is an open-source visual-inertial-leg odometry system that achieves low drift in real-time for legged robots across diverse terrains by online calibration and outlier rejection, outperforming existing methods.

Contribution

It introduces Cerberus, a novel VILO system with online kinematic calibration and contact outlier rejection, significantly reducing drift during high-speed locomotion.

Findings

Drift reduced to below 1% over long distances

Outperforms existing sensor-based methods in drift accuracy

Remains robust under impacts and occlusions

Abstract

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.