This paper presents results from the first two Space Shuttle test flights of the TriDAR vision system. TriDAR was developed as a proximity operations sensor for autonomous rendezvous and docking (AR&D) missions to noncooperative targets in space. The system does not require the use of cooperative markers, such as retro‐reflectors, on the target spacecraft. TriDAR includes a hybrid three‐dimensional (3D) sensor along with embedded model based tracking algorithms to provide six‐degree‐of‐freedom (6 DOF) relative pose information in real time. A thermal imager is also included to provide range and bearing information for far‐range rendezvous operations. In partnership with the Canadian Space Agency (CSA) and NASA, Neptec has space‐qualified the TriDAR vision system and integrated it on board Space Shuttle Discovery to fly as a detailed test objective (DTO) on the STS‐128 and STS‐131 missions to the International Space Station (ISS). The objective of the TriDAR DTO missions was to demonstrate the system's ability to perform acquisition and tracking of a known target in space autonomously and provide real‐time relative navigation cues. Knowledge (reference 3D model) about the target can be obtained on the ground or in orbit. Autonomous operations involved automatic acquisition of the ISS and real‐time tracking, as well as detection and recovery from system malfunctions and/or loss of tracking. © 2012 Wiley Periodicals, Inc.

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Neptec Design Group has developed a 3D automatic target recognition and pose estimation algorithm technology demonstrator in partnership with Canadian DND. This paper discusses the development of the algorithm to work with real sensor data. The recognition approach uses a combination of two algorithms in a multi-step process. The two algorithms provide uncorrelated metrics and are therefore using different characteristics of the target. This allows the potential target dataset to be reduced before the final selection is made. In a pre-processing phase, the object data is segmented from the surroundings and is re-projected onto an orthogonal grid to make the object shape independent of range. In the second step, a fast recognition algorithm is used to reduce the list of potential targets by removing unlikely cases. Then a more accurate, but slower and more sensitive, algorithm is applied to the remaining cases to provide another recognition metric while simultaneously computing a pose estimation. After passing some selfconsistency checks, the metrics from both algorithms are then combined to provide relative probabilities for each database object and a pose estimate. Development of the recognition and pose algorithm relied on processing of real 3D data from civilian and military vehicles. The algorithm evolved to be robust to occlusions and characteristics of real 3D data, including the use of different 3D sensors for generating database and test objects. Robustness also comes from the self-validating abilities and simultaneous pose estimation and recognition, along with the potential for computing error bounds on pose. Performance results are shown for pseudo-synthetic data and preliminary tests with a commercial imaging LIDAR.

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Neptec Design Group Ltd. has developed a 3D Automatic Target Recognition (ATR) and pose estimation technology demonstrator in partnership with the Canadian DND. The system prototype was deployed for field testing at Defence Research and Development Canada (DRDC) -Valcartier. This paper discusses the performance of the developed algorithm using 3D scans acquired with an imaging LIDAR. 3D models of civilian and military vehicles were built using scans acquired with a triangulation laser scanner. The models were then used to generate a knowledge base for the recognition algorithm. A commercial imaging LIDAR was used to acquire test scans of the target vehicles with varying range, pose and degree of occlusion. Recognition and pose estimation results are presented for at least 4 different poses of each vehicle at each test range. Results obtained with targets partially occluded by an artificial plane, vegetation and military camouflage netting are also presented. Finally, future operational considerations are discussed.

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