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 paper, 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 3D 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 3D 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.
翻译:人类倾向于用结构构建环境, 结构主要由平面组成。 从平面的交叉点产生直线。 线的参数比点具有更多的自由度。 因此, 基于线的结构从运动( SfM) 提供了更多环境信息。 在本文中, 我们用线( 递增 SfM) 为SfM 提供解决方案。 这些方法包括设计国家观察者, 以照相机的动态视觉系统看三维线。 我们首先展示一个模型, 使用球形坐标代表线瞬间矢量。 我们展示了这个参数有奇特性, 因此我们引入了一个更合适的模型, 来考虑线的瞬间和最短的观察线。 关于观察者, 我们提出了两种不同的方法。 第一个是用线线( 递增 SfM) 来为动态视觉系统提供一个不留记忆状态的艺术框架。 由于之前的机器人代理器状态是无障碍的 -- 进行三维环境绘图时, 第二种方法的目的是利用记忆来提高估计精确性和趋同速度。 两个模型和两个观察者在模拟和真实数据中使用的机器人。