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On Incremental Structure from Motion Using Lines

机译:关于使用线的运动的增量结构

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摘要

Humans tend to build environments with structure, which consists of mainly planar surfaces. From the intersection of planar surfaces arise straight lines. Lines have more degrees of freedom than points. Thus, line-based structure-from-motion (SfM) provides more information about the environment. In this article, we present solutions for SfM using lines, namely, incremental SfM. These approaches consist of designing state observers for a camera's dynamical visual system looking at a 3-D line. We start by presenting a model that uses spherical coordinates for representing the line's moment vector. We show that this parameterization has singularities, and, therefore, we introduce a more suitable model that considers the line's moment and shortest viewing ray. Concerning the observers, we present two different methodologies. The first uses a memory-less state-of-the-art framework for dynamic visual systems. Since the previous states of the robotic agent are accessible-while performing the 3-D mapping of the environment-the second approach aims at exploiting the use of memory to improve the estimation accuracy and convergence speed. The two models and the two observers are evaluated in simulation and real data, where mobile and manipulator robots are used.
机译:人类倾向于构建具有结构的环境,结构主要由平面组成。从平面的交点出现直线。线的自由度比点大。因此,基于线的运动结构 (SfM) 提供了有关环境的更多信息。在本文中,我们介绍了使用线的 SfM 解决方案,即增量 SfM。这些方法包括为相机的动态视觉系统设计状态观察器,以观察 3D 线。我们首先介绍一个模型,该模型使用球面坐标来表示线的力矩矢量。我们证明了这种参数化具有奇异性,因此,我们引入了一个更合适的模型来考虑线的矩和最短的观察光线。关于观察员,我们提出了两种不同的方法。第一种是将无内存的最先进的框架用于动态视觉系统。由于机器人代理的先前状态是可访问的,同时执行环境的 3D 映射,因此第二种方法旨在利用内存来提高估计精度和收敛速度。在模拟和真实数据中对两个模型和两个观察者进行评估,其中使用了移动机器人和机械手机器人。

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