arXiv 2504.174102025-04-24cs.RO

面向立体视觉里程计的偏差消除PnP：可证明一致的大规模定位

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

School of Data Science, Chinese University of Hong Kong, Shenzhen（数据科学学院，香港中文大学（深圳））；Australian Center for Robotics and School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney（机器人中心及航空航天、机械与机电工程学院，悉尼大学）

Guangyang Zeng, Yuan Shen, Ziyang Hong, Yuze Hong, Viorela Ila, Guodong Shi, Junfeng Wu

备注：: 10 pages, 7 figures
发表信息：: IEEE Robotics and Automation Letters ( Volume: 10, Issue: 11, November 2025)
DOI：: 10.1109/LRA.2025.3614050

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AI总结：

本研究提出具有可证明一致性的偏差消除加权PnP估计器及配套立体VO框架，在KITTI等数据集上验证了其在大规模环境和不稳定运动下的定位性能提升，为高不确定性机器人估计任务的信息筛选提供参考。

摘要：

本文首先提出一种用于立体视觉里程计（VO）的偏差消除加权（Bias-Eli-W）透视n点（PnP）估计器，该估计器具有可证明的一致性。具体而言，我们利用统计理论，开发了一种渐近无偏且具有$\boldsymbol{\frac{1}{\n}}$一致性的PnP估计器，该估计器考虑了变化的3D三角测量不确定性，确保相对位姿估计值随着特征数量的增加收敛于真值。接下来，在立体VO流程方面，我们提出了一个框架，该框架持续对当前特征进行三角测量以跟踪新帧，有效解耦了位姿误差与3D点误差之间的时间依赖关系。我们将Bias-Eli-W PnP估计器集成到所提出的立体VO流程中，产生协同效应，增强了对位姿估计误差的抑制效果。我们在KITTI和Oxford RobotCar数据集上验证了所提方法的性能。实验结果表明，我们的方法：1）在大规模环境中的相对位姿误差和绝对轨迹误差方面均实现了显著提升；2）在机器人运动不稳定且不可预测的情况下仍能提供可靠的定位。Bias-Eli-W PnP在立体VO中的成功应用，表明了信息筛选在具有高不确定性测量的机器人估计任务中的重要性，为以PnP为核心组件的各类应用提供了思路。

英文摘要：

In this paper, we first present a bias-eliminated weighted (Bias-Eli-W) perspective-n-point (PnP) estimator for stereo visual odometry (VO) with provable consistency. Specifically, leveraging statistical theory, we develop an asymptotically unbiased and $\sqrt {n}$-consistent PnP estimator that accounts for varying 3D triangulation uncertainties, ensuring that the relative pose estimate converges to the ground truth as the number of features increases. Next, on the stereo VO pipeline side, we propose a framework that continuously triangulates contemporary features for tracking new frames, effectively decoupling temporal dependencies between pose and 3D point errors. We integrate the Bias-Eli-W PnP estimator into the proposed stereo VO pipeline, creating a synergistic effect that enhances the suppression of pose estimation errors. 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 environments; 2) provides reliable localization under erratic and unpredictable robot motions. The successful implementation of the Bias-Eli-W PnP in stereo VO indicates the importance of information screening in robotic estimation tasks with high-uncertainty measurements, shedding light on diverse applications where PnP is a key ingredient.

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