Development of a practical 3D automatic target recognition and pose estimation algorithm

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.