Neptec has developed a vision system for the capture of non-cooperative objects on orbit. This system uses an active TriDAR sensor and a model based tracking algorithm to provide 6 degree of freedom pose information in real-time from mid range to docking. This system was selected for the Hubble Robotic Vehicle De-orbit Module (HRVDM) mission and for a Detailed Test Objective (DTO) mission to fly on the Space Shuttle.
TriDAR (triangulation + LIDAR) technology makes use of a novel approach to 3D sensing by combining triangulation and Time-of-Flight (ToF) active ranging techniques in the same optical path. This approach exploits the complementary nature of these sensing technologies. Real-time tracking of target objects is accomplished using 3D model based tracking algorithms developed at Neptec in partnership with the Canadian Space Agency (CSA). The system provides 6 degrees of freedom pose estimation and incorporates search capabilities to initiate and recover tracking. Pose estimation is performed using an innovative approach that is faster than traditional techniques. This performance allows the algorithms to operate in real-time on the TriDAR's flight certified embedded processor.
This paper presents results from simulation and lab testing demonstrating that the system's performance meets the requirements of a complete tracking system for on-orbit autonomous rendezvous and docking.
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.
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 self-consistency 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.