Model-free pose estimation using point cloud data

Abstract

A model-free pose (relative attitude and position) estimation process using point cloud data is developed to support envisioned autonomous satellite servicing missions. Unlike model-based pose estimation that typically requires a three-dimensional geometric model of a satellite to be serviced, the model-free pose estimation presented in this paper employs object features detected in situ using point cloud data. Its overall process is similar to existing model-free pose estimation algorithms in that it requires edge detection, object boundary identification, edge vector determination to form an orthogonal triad for attitude estimation, and object area and centroid calculation for position estimation with respect to the sensor. However, significant improvements have been made toward fully autonomous, model-free pose estimation by employing the Hough transform that allows robust line fitting of jagged edges resulting from object rotations and the discrete nature of point cloud data. In addition, a plane fitting algorithm using the homogeneous transformation was developed to compute plane normal vectors to help improve attitude estimation accuracy. Using both measured and simulated point cloud data of test objects, the performance of the developed model-free pose estimation process was examined. It was able to produce accurate position and attitude of the objects. Future effort will be needed to include more general geometric features such as circles, ellipses, and arcs to provide capabilities needed toward fully autonomous model-free pose estimation process.