A Factor Graph Approach to Multi-Camera Extrinsic Calibration on Legged Robots
This addresses the need for precise sensor calibration in legged robots to improve tasks like SLAM and state estimation, though it is incremental as it builds on existing factor graph and fiducial marker techniques.
The paper tackles the problem of calibrating the relative pose between multiple cameras and the base link of a quadruped robot, proposing a factor graph approach that jointly optimizes these poses using kinematics and fiducial markers, and shows quantitative performance comparisons with state-of-the-art methods on the HyQ robot.
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.