基于点和线的线性时间最优相机-机器人位姿估计
FUSE-PnPL: Flexible, Unified, Statistically Consistent, and Asymptotically Efficient Camera Pose Estimation from Points and Lines
AI总结：
本文提出了一种基于点和线特征的线性时间最优相机-机器人位姿估计算法 AOPnP(L)，通过统一噪声模型并采用两步估计方案求解最大似然问题，实现了理论最优性和线性时间复杂度，适用于高精度实时机器人位姿估计。
摘要：
相机位姿估计是机器人学中的一个基本问题。本文重点关注两个感兴趣的问题：首先，点和线特征具有互补的优势，设计一种能够有效融合它们的统一算法具有重要价值；其次，随着现代前端技术的发展，单张图像中可以存在大量特征，这为实现高精度的机器人位姿估计提供了潜力。基于这些观察，我们提出了一种基于点和线的线性时间最优相机-机器人位姿估计算法 AOPnP(L)。具体而言，我们用一条直线上的两个不同点来表示该直线，并统一了点和线测量的噪声模型，其中噪声被添加到图像中的二维点上。通过利用 Plucker 坐标进行直线参数化，我们为组合的点和线测量构建了一个最大似然（ML）问题。为了最优地解决该 ML 问题，AOPnP(L) 采用了一种两步估计方案。在第一步中，通过消除偏差，设计了一个能够收敛到真实位姿的一致估计。在第二步中，执行单次 Gauss-Newton 迭代以细化初始估计。AOPnP(L) 具有理论最优性，即其均方误差收敛于 Cramer-Rao 下界。此外，它具有线性时间复杂度。这些特性使其非常适合于要求高精度和实时的机器人位姿估计。我们进行了大量实验，验证了我们的理论发展，并展示了 AOPnP(L) 在静态定位和动态里程计系统中的优越性。
英文摘要：
Camera pose estimation from 3D-2D correspondences is fundamental to robotics. Three practical considerations motivate this work. First, point and line features provide complementary geometric information, motivating a unified estimator. Second, the number and composition of available features vary across scenarios, requiring support for minimal and arbitrary non-minimal configurations. Third, modern visual front ends extract abundant features from individual images, enabling highly accurate pose estimation. We propose FUSE-PnPL, a flexible and unified camera pose estimator that is statistically consistent and asymptotically efficient. Specifically, we represent each line using two distinct points and adopt a unified image-space noise model for point and line measurements. By employing Plücker coordinates for line parameterization, we explicitly characterize the propagation of image endpoint noise into line residuals and formulate a joint maximum likelihood (ML) estimation problem for combined point and line measurements. To efficiently solve this problem, FUSE-PnPL employs a two-stage estimation scheme. First, noise variance estimation and bias elimination yield a consistent initial pose estimate. A single Gauss-Newton iteration then refines this estimate, achieving asymptotic efficiency as its mean squared error asymptotically attains the Cramér-Rao lower bound. Moreover, the proposed estimator has linear computational complexity with respect to the number of correspondences, making it suitable for high-precision and real-time robot pose estimation. Extensive experiments on synthetic data and real-world benchmarks validate the theoretical properties of FUSE-PnPL and demonstrate its advantages in both static global localization and dynamic SLAM systems.
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