Feature Detection for Pose Estimation

Abstract

A pose (relative attitude and position) estimation approach to support proximity operations such as autonomous landing of unmanned air vehicles or spacecraft on-orbit servicing is investigated in this paper. An algorithm is developed based on a feature detection capability using the point cloud data that a flash lidar (light detection and ranging) produces. The feature refers to an edge, a corner, or a distinct geometrical shape of an object. Once features are detected, orthogonal triads that are attached to the object are constructed using them. Pose estimation is then performed based on the orientation of the triads with respect to the lidar sensor frame. Since the algorithm employs detected features, it allows pose estimation of an object from a single set of point cloud data. In contrast, the conventional iterative closest point based algorithms typically require two sets of point cloud data. The estimated pose thus represents the changes in pose between the two data sets, leaving the pose of the object with respect to the sensor still to be determined. This featurebased pose estimation capability performs directly an initial pose acquisition with respect to the sensor. The algorithm may also help reduce the number of data points required for pose estimation since a small, selected feature of an object within the sensor field of view can be employed to estimate the pose. Employing the algorithm developed, pose estimation tests were conducted and their preliminary results are presented, which were obtained using a flash lidar in a laboratory testing environment.