发表机构
Ericsson Research; University of Zaragoza（爱立信研究院; 萨拉戈萨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对部分标定绝对位姿估计的空白，利用IMU重力向量与特征局部几何构建两种高效求解器，实现更少样本、更低成本的快速精准定位与焦距估计。
AI 中文摘要
惯性测量单元（IMU）现已成为智能手机、无人机、扩展现实（XR）头戴设备等多数消费电子设备的标配。通过融合视觉与惯性数据，定位系统相比仅依赖视觉或仅依赖IMU的方法，在速度与鲁棒性上显著提升。然而，传统位姿估计方法未能利用SIFT等特征描述符中嵌入的局部几何信息。近期研究已证实，将该信息用于相对与绝对位姿估计具有优势，但将其应用于部分标定绝对位姿估计的研究仍属空白。本文利用IMU数据得到的重力向量与特征诱导的局部几何，推导了用于联合估计绝对位姿与焦距的新型约束，以此构建两种高效求解器：UP1PfAC（需1组仿射对应关系）与UP2PfORI（需2个旋转协变特征）。不同于需4组点对应关系的传统半标定绝对位姿方法，所提求解器所需样本更少、计算成本更低，可简化现代类RANSAC框架中的鲁棒估计流程。我们在大规模公开数据集上将所提求解器与当前最优方法对比，证明本文方法可实现快速且精准的定位与焦距估计。
英文摘要
Inertial measurement units (IMUs) are now standard in most consumer devices, such as smartphones, drones, and extended reality (XR) headsets. By fusing visual and inertial data, localization systems gain significantly in speed and robustness compared to vision-only or IMU-only approaches. However, traditional pose estimation methods fail to utilize the local geometric information embedded in feature descriptors like SIFT. Recent work has proved the advantages of leveraging this information for relative and absolute pose estimation, but its application to partially calibrated absolute pose estimation remains unexplored. In this paper, we derive novel constraints for joint estimation of absolute pose and focal length, making use of a gravity vector obtained from IMU data and the feature-induced local geometry, which we use to construct two efficient solvers: UP1PfAC, that operates given a single affine correspondence and UP2PfORI, which requires two orientation-covariant features. Unlike traditional, semi-calibrated absolute pose methods requiring four point correspondences, our solvers benefit from fewer samples and lower computational cost, simplifying robust estimation in modern RANSAC-like frameworks. We evaluate the proposed solvers against the state-of-the-art on large-scale public datasets and demonstrate that our method achieves fast and accurate localization and focal length estimation.
CommentsEuropean Conference on Computer Vision 2026
DOI：10.1007/978-3-032-37211-6_19
