提出虚拟点方法，让传统相机定位算法能处理多摄像头系统。
Virtual-point-based Solutions to Handle Generalized Absolute Pose Problem
- 用虚拟点统一标准与广义位姿问题，改造现有算法
- 三种新算法在精度、全局最优和效率上全面超越已有方案
- 适合需要多视角定位的机器人与自动驾驶场景
多摄像头系统在机器人与自主导航中应用日益广泛，因其视野广、灵活且容错性强。然而，现有位姿求解器无法处理多个投影中心的问题。本文提出一种虚拟点建模方法，连接标准位姿问题与广义位姿问题，构建统一框架，将现有位姿求解器转化为广义位姿求解器。基于此框架，我们推导出三种基于虚拟点的广义位姿求解器：VGPc（使用Cayley参数化）、VGPq（使用四元数）和VGPr（使用旋转矩阵）。大量实验表明，所提求解器继承了原有PnP算法的精度与效率，同时显著优于现有广义求解器：其中VGPc在异方差噪声下表现更优，VGPq保持全局最优性，而VGPr在不损失精度的前提下实现更高计算效率。
原文摘要 · Abstract (English)
Multi-camera systems are increasingly adopted in robotics and autonomous navigation for their wide field of view, flexibility, and fault tolerance. Nevertheless, existing PnP solvers fail to handle multiple projection centers. This paper introduces a virtual point formulation that bridges the standard PnP and generalized pose problems, enabling a unified pipeline that transforms existing PnP solvers into generalized pose solvers. Based on this framework, we derive three Virtual-point-based Generalized Pose solvers, namely VGPc, VGPq, and VGPr, leveraging Cayley, quaternion, and rotation-matrix parameterizations, respectively. Extensive experiments demonstrate that the proposed solvers inherit the accuracy and efficiency of original PnP algorithms while significantly outperforming existing generalized solvers. Specifically, VGPc achieves higher estimation accuracy under heteroscedastic noise conditions, VGPq maintains global optimality, whereas VGPr provides superior computational efficiency without accuracy degradation.
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