CosySlam: investigating object-level SLAM for detecting locomotion surfaces

While blindfolded legged locomotion has demonstrated impressive capabilities in the last few years, further progresses are expected from using exteroceptive perception to better adapt the robot behavior to the available surfaces of contact. In this paper, we investigate whether mono cameras are suitable sensors for that aim. We propose to rely on object-level SLAM, fusing RGB images and inertial measurements, to simultaneously estimate the robot balance state (orientation in the gravity field and velocity), the robot position, and the location of candidate contact surfaces. We used CosyPose, a learning-based object pose estimator for which we propose an empirical uncertainty model, as the sole front-end of our visual inertial SLAM.We then combine it with inertial measurements which ideally complete the system observability, although extending the proposed approach would be straightforward (e.g. kinematic information about the contact, or a feature based visual front end).We demonstrate the interest of object-based SLAM on several locomotion sequences, by some absolute metrics and in comparison with other mono SLAM

CosySlam: investigating object-level SLAM for detecting locomotion surfaces

While blindfolded legged locomotion has demonstrated impressive capabilities in the last few years, further progresses are expected from using exteroceptive perception to better adapt the robot behavior to the available surfaces of contact. In this paper, we investigate whether mono cameras are suitable sensors for that aim. We propose to rely on object-level SLAM, fusing RGB images and inertial measurements, to simultaneously estimate the robot balance state (orientation in the gravity field and velocity), the robot position, and the location of candidate contact surfaces. We used CosyPose, a learning-based object pose estimator for which we propose an empirical uncertainty model, as the sole front-end of our visual inertial SLAM.We then combine it with inertial measurements which ideally complete the system observability, although extending the proposed approach would be straightforward (e.g. kinematic information about the contact, or a feature based visual front end).We demonstrate the interest of object-based SLAM on several locomotion sequences, by some absolute metrics and in comparison with other mono SLAM

CosySlam: investigating object-level SLAM for detecting locomotion surfaces

While blindfolded legged locomotion has demonstrated impressive capabilities in the last few years, further progresses are expected from using exteroceptive perception to better adapt the robot behavior to the available surfaces of contact. In this paper, we investigate whether mono cameras are suitable sensors for that aim. We propose to rely on object-level SLAM, fusing RGB images and inertial measurements, to simultaneously estimate the robot balance state (orientation in the gravity field and velocity), the robot position, and the location of candidate contact surfaces. We used CosyPose, a learning-based object pose estimator for which we propose an empirical uncertainty model, as the sole front-end of our visual inertial SLAM.We then combine it with inertial measurements which ideally complete the system observability, although extending the proposed approach would be straightforward (e.g. kinematic information about the contact, or a feature based visual front end).We demonstrate the interest of object-based SLAM on several locomotion sequences, by some absolute metrics and in comparison with other mono SLAM

Model predictive control under hard collision avoidance constraints for a robotic arm

We design a method to control the motion of a manipulator robot while strictly enforcing collision avoidance in a dynamic obstacle field. We rely on model predictive control while formulating collision avoidance as a hard constraint. We express the constraint as the requirement for a signed distance function to be positive between pairs of strictly convex objects. Among various formulations, we provide a suitable definition for this signed distance and for the analytical derivatives needed by the numerical solver to enforce the constraint. The method is completely implemented on a manipulator "Panda" robot, and the efficient open-source implementation is provided along with the paper. We experimentally demonstrate the efficiency of our approach by performing dynamic tasks in an obstacle field while reacting to non-modeled perturbations