# Virtual-point-based Solutions to Handle Generalized Absolute Pose Problem Source: [Virtual-point-based Solutions to Handle Generalized Absolute Pose Problem]() Collection index: [llms.txt]() Data updated: 2026-06-10T05:15:22.625Z Authors: Bin Li, Banglei Guan, Shunkun Liang, Yang Shang Published: 2026-06-08T10:02:03.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Pose Estimation on DMAIS 1.0 (flight trial)](): Pose Estimation; DMAIS 1.0 (flight trial); Positional Error (m): 0.046. - [Pose Estimation on DMAIS flight trial 1.0](): Pose Estimation; DMAIS flight trial 1.0; Positional Error (m): 0.027. ## Original sources and implementation [Original paper]() [PDF]()