Robust Vehicle Localization for Spherical Camera Models: Solution, Framework, and Verification

作者：Yunfeng Lu, Yanmei Jiao, Dibin Zhou, Xiumei Li, Rong Xiong, Yue Wang · 发表于：IEEE Transactions on Intelligent Transportation Systems · 年份：2025 · DOI：10.1109/tits.2025.3558257 · 被引用次数：1 · 研究领域：Robotics and Sensor-Based Localization、Advanced Vision and Imaging、Advanced Image and Video Retrieval Techniques

Vehicle visual localization uses vision sensors to capture environmental information, enabling precise localization of autonomous vehicles within their surroundings. However, current visual localization methods generally have some shortcomings: on one hand, they are limited by the camera’s field of view, on the other hand, their robustness is often inadequate under challenging conditions such as lighting changes, long-term scene changes, or occlusions. To address these issues, we formulate a general spherical camera model for both fisheye and panoramic cameras and propose a minimal solution for pose estimation using this model based on vehicle motion characteristic. The minimal solution cannot filter outliers, so a robust estimation framework is necessary. For outlier-rejection, we introduce two frameworks: a probabilistic optimal RANSAC and a globally optimal graph-based framework. We conduct a probabilistic analysis of the RANSAC to demonstrate its enhanced robustness given by the proposed minimal solution. To achieve robustness to extreme outliers (higher than 90%), we decouple the rotation and translation space through the minimal solution to construct maximum consensus graph for the two sub-problems. We then employs a maximum clique search algorithm to find the optimal solutions, achieving deterministic convergence while maintaining real-time performance. Extensive experiments with synthetic data, real-world fisheye images, and 360∘panoramic images validate the robustnes...