Legged robots are becoming popular not only in research, but also in industry, where they can demonstrate their superiority over wheeled machines in a variety of applications. Either when acting as mobile manipulators or just as all-terrain ground vehicles, these machines need to precisely track the desired base and end-effector trajectories, perform Simultaneous Localization and Mapping (SLAM), and move in challenging environments, all while keeping balance. A crucial aspect for these tasks is that all onboard sensors must be properly calibrated and synchronized to provide consistent signals for all the software modules they feed. In this paper, we focus on the problem of calibrating the relative pose between a set of cameras and the base link of a quadruped robot. This pose is fundamental to successfully perform sensor fusion, state estimation, mapping, and any other task requiring visual feedback. To solve this problem, we propose an approach based on factor graphs that jointly optimizes the mutual position of the cameras and the robot base using kinematics and fiducial markers. We also quantitatively compare its performance with other state-of-the-art methods on the hydraulic quadruped robot HyQ. The proposed approach is simple, modular, and independent from external devices other than the fiducial marker.
More recently, Bl "ochlinger al @cite_7 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. The objective of the cost function includes a set of 33 parameters, including: link lengths, encoder offsets, time offsets, and also the robot-camera relative pose. Since the focus was more on finding the kinematics parameters, no results are shown for the robot-camera calibration. The performance on link lengths is assessed by comparison with the CAD specifications, yielding a margin of 5 .
@cite_7
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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 xed time window in order to estimate the 33 unknown parameters. This is eciently 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.