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On Incremental Structure from Motion Using Lines
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AI
DOI：10.1109/TRO.2021.3085487.png)
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Abstract
En 中文
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.
Keywords:
Observers
Estimation
Robots
Cameras
Kalman filters
Visualization
Visual servoing
Computer vision for automation
incremental structure-from-motion (SfM)
visual-based navigation
visual servoing
Journal
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10.5
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3.3K
Citations:
2.8W