Automated identification and classification of land vehicles in 3D LADAR data

摘要

3D sensors provide unique opportunities for performing automatic target recognition (ATR). We describe an automated system that exploits 3D target geometry to perform rapid and robust ATR in the domain of military and civilian ground vehicles. The system identifies specific vehicles by comparing 3D LADAR data to model based LADAR predictions from highly-detailed CAD models with articulating parts. In addition to performing identification, the system solves for whole vehicle six-degree-of-freedom pose as well as detailed target articulation state. Because of its specificity, the identification system performs high probability of correct identification across a library of ～ 100 target models and exhibits robustness to occlusion, clutter and sensor noise. This identification capability is currently being extended for the purpose of classifying generic vehicle types (tanks, trucks, air defense units, etc.). The goal of the extended system is to perform vehicle classification before performing vehicle identification. This methodology provides a more flexible model-based ATR capability because it obviates the need for modeling all possible target types in advance. Classification enables the recognition of novel targets which have not been modeled or previously observed by the system. We classify targets based on general 3D morphology and characteristic 3D relationships between observed parts and features. This approach exploits the distinctive anatomy of different functional target types to achieve a more flexible and extensible target recognition capability.