On Incremental Structure-from-Motion using Lines

TL;DR

This paper develops incremental structure-from-motion methods based on lines, introducing new models and observers that leverage line geometry and memory to improve 3D environment reconstruction.

Contribution

It proposes novel line-based SfM models and observers, including a memory-augmented approach, for enhanced 3D mapping accuracy and convergence.

Findings

Memory-based observer improves estimation accuracy.

Models tested successfully on simulation and real robots.

Line-based SfM provides richer environmental information.

Abstract

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.