论文标题
基于1点RANSAC的地面对象姿势估计方法
1-Point RANSAC-Based Method for Ground Object Pose Estimation
论文作者
论文摘要
求解透视图N点（PNP）问题是估计对象姿势的传统方式。给定的离群值污染数据，在基于RANSAC的方案中，用n = {3，4}的PNP算法计算一个对象的姿势。但是，计算复杂性随着n而大大增加，高复杂性对设备造成了严重的压力，这应实时估计多个物体构成。在本文中，我们提出了一种基于1分兰萨克的有效方法，用于估计地面上的物体姿势。在提出的方法中，通过使用接地对象假设和2D对象边界框作为附加观察结果来计算姿势，从而实现基于RANSAC的方法之间的最快性能。此外，由于该方法遭受了其他信息的误差，因此我们提出了一种层次稳健的估计方法，用于抛光粗糙的姿势估计，并以粗略的方式发现更多的嵌入者。合成和现实世界数据集的实验证明了该方法的优越性。
Solving Perspective-n-Point (PnP) problems is a traditional way of estimating object poses. Given outlier-contaminated data, a pose of an object is calculated with PnP algorithms of n = {3, 4} in the RANSAC-based scheme. However, the computational complexity considerably increases along with n and the high complexity imposes a severe strain on devices which should estimate multiple object poses in real time. In this paper, we propose an efficient method based on 1-point RANSAC for estimating a pose of an object on the ground. In the proposed method, a pose is calculated with 1-DoF parameterization by using a ground object assumption and a 2D object bounding box as an additional observation, thereby achieving the fastest performance among the RANSAC-based methods. In addition, since the method suffers from the errors of the additional information, we propose a hierarchical robust estimation method for polishing a rough pose estimate and discovering more inliers in a coarse-to-fine manner. The experiments in synthetic and real-world datasets demonstrate the superiority of the proposed method.
