无需点对应关系，用模式匹配实现多维视觉姿态估计
Correspondence-Free Pose Estimation with Patterns: A Unified Approach for Multi-Dimensional Vision
- 通过模式特征构建方程组，消除未知数分离姿态与对应关系
- 支持3D-3D、3D-2D、2D-2D等各类非线性变换下的姿态估计
- 适用于仿真与实测数据，对复杂投影变换鲁棒
6D姿态估计是机器人视觉的核心问题。相较于依赖点对应或其鲁棒版本的方法，无对应关系方法更具灵活性。现有方法多依赖特征对齐或端到端回归，本文提出一种新方法及其实用算法，核心思想是通过加法过程消除未知量，将姿态估计与对应关系解耦。将点集视为模式，引入特征函数描述这些模式，建立足够数量的优化方程。该方法可处理透视投影等非线性变换，覆盖3D-to-3D、3D-to-2D、2D-to-2D等多种姿态估计场景。在仿真和实际数据上的实验结果验证了方法的有效性。
原文摘要 · Abstract (English)
6D pose estimation is a central problem in robot vision. Compared with pose estimation based on point correspondences or its robust versions, correspondence-free methods are often more flexible. However, existing correspondence-free methods often rely on feature representation alignment or end-to-end regression. For such a purpose, a new correspondence-free pose estimation method and its practical algorithms are proposed, whose key idea is the elimination of unknowns by process of addition to separate the pose estimation from correspondence. By taking the considered point sets as patterns, feature functions used to describe these patterns are introduced to establish a sufficient number of equations for optimization. The proposed method is applicable to nonlinear transformations such as perspective projection and can cover various pose estimations from 3D-to-3D points, 3D-to-2D points, and 2D-to-2D points. Experimental results on both simulation and actual data are presented to demonstrate the effectiveness of the proposed method.
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