<title>Development of a practical 3D automatic target recognition and pose estimation algorithm</title>

Abstract

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.

Ground Target Recognition Using Rectangle Estimation

“…1.5 points/m 2 ). In [10,16,30,31], there are typically several hundreds of samples on a target. Typically, the data is collected in a forward-looking perspective, while in [4] and [28] down-looking perspective data is considered.…”

“…Vehicle recognition using laser radar Several ATR methods or systems for recognition of military ground vehicles based on laser radar data have been proposed over the years [4,10,30,31,33,35]. During the last years, also ATR of civilian personal cars, mainly for traf c monitoring, have been proposed [16,28,34].…”

“…The rst step is used to reduce the list of potential targets. Then, the remaining targets are subject to 3D matching with library models, which are represented by CAD models [4,10], some representation generated from CAD models [31,33,35] or 3D scatter data [10,30,34]. The ATR approach [35] is further evaluated in [18].…”

“…The high resolution in angle-angle-range makes 3D imaging possible and due to the short wavelength, in general 0. [5][6][7][8][9][10] m, detailed range images of objects and background can be obtained. Due to the high resolution, even at km distances, details of a target can be resolved.…”

“…Usually the rst step consists of fast feature extraction or silhouette calculations [10,31,33,34]. The feature extraction can retrieve geometrical properties of the target [4], lowerdimensional properties [35] or more abstract features like spin image representation [16,30] (see [20] for description of spin images).…”