Self-Contained Calibration of an Elastic Humanoid Upper Body Using Only a Head-Mounted RGB Camera

Abstract

When a humanoid robot performs a manipulation task, it first makes a model of the world using its visual sensors and then plans the motion of its body in this model. For this, precise calibration of the camera parameters and the kinematic tree is needed. Besides the accuracy of the calibrated model, the calibration process should be fast and self-contained, i.e., no external measurement equipment should be used. Therefore, we extend our prior work on calibrating the elastic upper body of DLR's Agile Justin by now using only its internal head-mounted RGB camera. We use simple visual markers at the ends of the kinematic chain and one in front of the robot, mounted on a pole, to get measurements for the whole kinematic tree. To ensure that the task-relevant cartesian error at the end-effectors is minimized, we introduce virtual noise to fit our imperfect robot model so that the pixel error has a higher weight if the marker is further away from the camera. This correction reduces the cartesian error by more than 20 %, resulting in a final accuracy of 3.9 mm on average and 9.1 mm in the worst case. This way, we achieve the same precision as in our previous work [1], where an external cartesian tracking system was used.

Learning-Based Real-Time Torque Prediction for Grasping Unknown Objects with a Multi-Fingered Hand

“…We evaluate our approach on our precisely calibrated [22] humanoid robot Agile Justin [23] by incorporating it into our grasping pipeline: For a given object, we first observe an incomplete 3D model using a Kinect depth camera [24]. Via shape completion, the observation is completed and used to predict a stable grasp via our grasping network [20].…”