Foot-Eye Calibration of Legged Robot Kinematics

Abstract

Legged robots rely on an accurate calibration of the system's kinematics for reliable motion tracking of dynamic gaits and for precise foot placement when moving in rough terrain. In our automatic foot-eye calibration approach, a monocular camera is attached to the robot and observes the robot's moving feet, which are equipped with Augmented Reality (AR) markers. The measurements are used to formulate a non-linear least squares problem over a fixed time window in order to estimate the 33 unknown parameters. This is efficiently solved with the Levenberg-Marquardt algorithm and we get estimates for both the kinematic and the camera parameters. The approach is successfully evaluated on a real quadruped robot.

A Factor Graph Approach to Multi-camera Extrinsic Calibration on Legged Robots

“…More recently, Blöchlinger et al [5] applied the same idea to the quadruped robot StarlETH. They used a couple of circular fiducial marker arrays attached to the front feet and minimized the reprojection error between the detected markers (with a monocular camera) and their projection using forward kinematics.…”

A Two-Stage Perception System Calibration Algorithm for Quadruped Robots Using Factor Graph

“…To estimate the extrinsic parameters, Camurri et al [5] set up a motion capture system and the augmented reality library ArUco. By fixing calibration boards on the feet, Blöchliger et al [6] proposed an automatic extrinsic calibration approach by formulating a non-linear least squares problem using visual measurements and estimated 33 unknown parameters. With a similar mechanical setup, Reinke et al [7] proposed a factor graph-based approach which jointly optimized the mutual position of cameras and the robot base using kinematics and fiducial markers.…”