Robust pose determination for autonomous docking

This paper describes current work at the Oak Ridge National Laboratory to develop a robotic vision system capable of recognizing designated objects by their intrinsic geometry. This method, based on single camera vision, combines point features and a model-based technique using geometric feature matching for the pose calculation. In this approach, 2-D point features are connected into higher-order shapes and then matched with corresponding features of the model. Pose estimates are made using a closed-form point solution based on model features of four coplanar points. Rotations are represented by quaternions that simplify the calculations in determining the least squares solution for the coordinate transformation. This pose determination method including image acquisition, feature extraction, feature correspondence, and pose calculation has been implemented on a real-time system using a standard camera and image processing hardware. Experimental results are given for relative error measurements.