This paper presents a novel method for pair-wise range image registration, a backbone task in world modeling, parts inspection and manufacture, object recognition, pose estimation, robotic navigation, and reverse engineering. The method finds the most suitable homogeneous transformation matrix between two constructed range images to create a more complete 3D view of a scene. The proposed solution integrates a ray casting-based fitness estimation with a global optimization method called improved self-adaptive differential evolution. This method eliminates the fine registration steps of the well-known iterative closest point (ICP) algorithm used in previously proposed methods, and thus, is the first direct global registration algorithm. With its parallel implementation potential, the ray casting-based algorithm speeds up the fitness calculation for the global optimization method, which effectively exploits the search space to find the best transformation solution. The integration was successfully implemented in a parallel paradigm on a multi-core computer processor to solve a simultaneous 3D localization problem. The fast, accurate, and robust results show that the proposed algorithm significantly improves on the registration problem over state-of-the-art algorithms.

中文翻译：

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全球光线投射范围图像配准

本文提出了一种新颖的成对范围图像配准方法，世界建模，零件检查和制造，目标识别，姿态估计，机器人导航和逆向工程中的骨干任务。该方法在两个构造的距离图像之间找到最合适的齐次变换矩阵，以创建场景的更完整的3D视图。提出的解决方案将基于射线投射的适应度估计与称为优化的自适应差分进化的全局优化方法集成在一起。该方法消除了先前提出的方法中使用的众所周知的迭代最近点（ICP）算法的精细配准步骤，因此是第一种直接全局配准算法。凭借其并行实施的潜力，基于射线投射的算法加快了全局优化方法的适应度计算，从而有效地利用了搜索空间来找到最佳的变换解决方案。集成已成功在多核计算机处理器上以并行范例实现，以解决同时发生的3D定位问题。快速，准确和鲁棒的结果表明，与现有算法相比，所提出的算法在注册问题上有了显着改进。