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Model-based recognition of 3D articulated target using ladar range data

机译：使用激光距离数据基于模型的3D关节目标识别

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Ladar is suitable for 3D target recognition because ladar range images can provide rich 3D geometric surface information of targets. In this paper, we propose a part-based 3D model matching technique to recognize articulated ground military vehicles in ladar range images. The key of this approach is to solve the decomposition and pose estimation of articulated parts of targets. The articulated components were decomposed into isolate parts based on 3D geometric properties of targets, such as surface point normals, data histogram distribution, and data distance relationships. The corresponding poses of these separate parts were estimated through the linear characteristics of barrels. According to these pose parameters, all parts of the target were roughly aligned to 3D point cloud models in a library and fine matching was finally performed to accomplish 3D articulated target recognition. The recognition performance was evaluated with 1728 ladar range images of eight different articulated military vehicles with various part types and orientations. Experimental results demonstrated that the proposed approach achieved a high recognition rate. (C) 2015 Optical Society of America.
机译：Ladar适用于3D目标识别，因为Ladar范围图像可以提供目标的丰富3D几何表面信息。在本文中，我们提出了一种基于零件的3D模型匹配技术来识别雷达范围图像中的铰接地面军车。该方法的关键是解决目标关节部分的分解和姿态估计。根据目标的3D几何特性（例如表面点法线，数据直方图分布和数据距离关系），将关节组件分解为孤立的零件。这些独立部分的相应姿势是通过枪管的线性特性估算的。根据这些姿势参数，将目标的所有部分与库中的3D点云模型大致对齐，并最终执行精细匹配以完成3D关节目标识别。通过使用八种不同零件类型和方向的八种不同铰接式军车的1728个激光雷达图像来评估识别性能。实验结果表明，该方法取得了较高的识别率。 （C）2015年美国眼镜学会。

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