Generalized Maximum Likelihood Estimation for Perspective-n-Point Problem

作     者：Zhan, Tian Xu, Chunfeng Zhang, Cheng Zhu, Ke 

作者机构：Beijing Inst Technol Sch Aerosp & Engn Beijing 100811 Peoples R China 

出 版 物：《IEEE ROBOTICS AND AUTOMATION LETTERS》 (IEEE Robot. Autom.)

年 卷 期：2025年第10卷第2期

页      面：1752-1759页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

主　　题：Cameras Uncertainty Noise Maximum likelihood estimation Covariance matrices Accuracy Noise measurement Location awareness Anisotropic Pose estimation Localization vision-based navigation probability and statistical methods 

摘      要：The Perspective-n-Point (PnP) problem has been widely studied in the literature and applied in various vision-based pose estimation scenarios. However, most existing methods ignore the anisotropy uncertainty of observations, as demonstrated in several real-world datasets in this letter. This oversight may lead to suboptimal and inaccurate estimation, particularly in the presence of noisy observations. To this end, we propose a generalized maximum likelihood PnP solver, named GMLPnP, that minimizes the determinant criterion by iterating the generalized least squares procedure to estimate the pose and uncertainty simultaneously. Further, the proposed method is decoupled from the camera model. Results of synthetic and real experiments show that our method achieves better accuracy in common pose estimation scenarios, GMLPnP improves rotation/translation accuracy by 4.7%/2.0% on TUM-RGBD and 18.6%/18.4% on KITTI-360 dataset compared to the best baseline. It is more accurate under very noisy observations in a vision-based UAV localization task, outperforming the best baseline by 34.4% in translation estimation accuracy.