Bias-Eliminated PnP for Stereo Visual Odometry: Provably Consistent and Large-Scale Localization

In this letter, we first present a bias-eliminated weighted (Bias-Eli-W) perspective-n-point (PnP) estimator for stereo visual odometry (VO) with provable consistency. Specifically, we develop a $\sqrt{n}$-consistent PnP estimator that accounts for 3D point uncertainties, ensuring that the relative pose estimate converges to the true value as the feature number increases. Next, on the stereo VO pipeline side, we propose a framework that triangulates current features for tracking new frames, decoupling temporal dependencies between pose and 3D point errors. We integrate the Bias-Eli-W PnP estimator into the proposed pipeline, creating a synergistic effect that enhances the accuracy of pose estimation. We validate the performance of our method on the KITTI and Oxford RobotCar datasets. Experimental results demonstrate that our method: 1) achieves significant improvements in both relative pose error and absolute trajectory error in large-scale scenarios; 2) provides reliable localization under erratic motions. The successful implementation of the Bias-Eli-W PnP in stereo VO indicates the importance of information screening in robotic estimation tasks, shedding light on diverse applications where PnP is a key ingredient.