Model-Based Tracking by Classification in a Tiny Discrete Pose Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent LIDAR scanning region selection for satellite pose estimation
Computer Vision and Image Understanding
Near-optimal selection of views and surface regions for ICP pose estimation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
CSCA-based expectivity indices for LIDAR computer vision
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
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This paper presents a method for determining the 3-D pose of a known object with the intent of autonomously controlling space hardware systems. Operations of interest include satellite on-orbit servicing and orbit transfer. Using a specially designed, adaptable vision server, stereo images are acquired and edge data is extracted. Corresponding points are found in the left and right edge data and three dimensional information is computed. This three dimensional data is then registered to a model of the object in such a way as to minimize the distance between the two data sets. Once the model is aligned with the data the full three dimensional pose of the object is known; this information can then be used by a robot controller to compute a path to the object. The process runs continuously giving the controller up to date information. Experimental results presented here show the capabilities and robustness of the system.