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.
We examine the problem of determining the relative time delay between IMU and camera data streams. The primary difficulty is that the correspondences between measurements from the sensors are not initially known, and hence, the time delay cannot be computed directly. We instead formulate time delay calibration as a registration problem, and introduce a calibration algorithm that operates by aligning curves in a three-dimensional orientation space. Results from simulation studies and from experiments with real hardware demonstrate that the delay can be accurately calibrated. cessing, integration, filtering, etc. If the delays are incorrectly estimated, then any fusion algorithm will produce suboptimal results. At best, this will degrade the accuracy of the output—in the worst case, it may lead to significant navigation errors. In this paper, we examine the problem of determining the relative time delay between inertial and visual sensor measure- ments. It may be possible in some cases to directly synchronize the sensors using a hardware timing signal. Our interest here is primarily in low-cost "black box" IMUs and cameras, for which direct time synchronization is not possible. We assume that there is a constant but unknown time shift (delay) when the different sensor measurements arrive and are timestamped at a centralized receiver.