With the advancement of visual sensing systems, computer vision is playing an increasingly important role in autonomous driving and robot navigation. Relative pose estimation in multi-camera systems is essential for accurate vehicle localization and environment perception, demanding high real-time performance and robustness. Existing methods, however, often involve high computational costs and rely heavily on abundant feature matches, limiting their applicability in time-sensitive driving scenarios. To address these limitations, this paper introduces a unified framework for efficient relative pose estimation, built upon a novel translation parameterization and first-order rotation approximation. Within this framework, we propose three efficient minimal solvers specifically designed for autonomous vehicles. The first solver integrates the vertical direction prior from Inertial Measurement Units (IMUs), the second utilizes the rotation axis direction prior during steering maneuvers, and the third is designed for planar motion - a realistic assumption for ground vehicles operating on structured roads. By reducing both the minimal number of point correspondences and the algebraic complexity, our methods enable faster hypothesis generation within RANSAC-based pipelines, improving suitability for real-time systems. Extensive experiments on synthetic datasets and the KITTI autonomous driving benchmark demonstrate that the proposed solvers achieve a favorable balance between speed and accuracy compared to existing state-of-the-art algorithms.

翻译：随着视觉传感系统的发展，计算机视觉在自动驾驶和机器人导航中发挥着日益重要的作用。多相机系统中的相对位姿估计是实现精确车辆定位与环境感知的关键，对高实时性和鲁棒性有严格要求。然而，现有方法通常计算成本高昂且严重依赖充足的匹配特征，限制了其在时间敏感型驾驶场景中的适用性。为应对这些局限，本文提出了一种基于新型平移参数化与一阶旋转逼近的高效相对位姿估计统一框架。在此框架内，我们针对自动驾驶车辆设计了三种高效最小求解器：第一种融合惯性测量单元的垂直方向先验，第二种在转向机动中利用旋转轴方向先验，第三种专为平面运动（结构化道路上地面车辆的现实假设）设计。通过减少最小点对应数量并降低代数复杂度，我们的方法能在RANSAC管线中加速假设生成，从而增强实时系统的适用性。在合成数据集和KITTI自动驾驶基准上的大量实验表明，与现有最优算法相比，所提求解器在速度与精度之间实现了有利平衡。