Efficient Minimal Solvers for Relative Pose Estimation in Autonomous Driving Applications
Existing relative pose estimation algorithms incur high computational costs and rely heavily on numerous feature matches, making them ill-suited for the real-time and robustness demands of autonomous driving. This work proposes a unified and efficient framework for relative pose estimation that introduces a novel translation parameterization and a first-order rotation approximation to derive three minimal solvers tailored for ground vehicles. By integrating multi-source priors—such as IMU-provided gravity direction, rotational axis constraints during steering, and the planar motion assumption—the method substantially reduces both the required number of point correspondences and algebraic complexity. Experiments on synthetic data and the KITTI benchmark demonstrate that the proposed approach achieves a superior trade-off between accuracy and speed compared to state-of-the-art methods.