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Visual SLAM and Structure from Motion in Dynamic Environments: A Survey

机译：动态环境中运动的视觉SLAM和结构：一项调查

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摘要

In the last few decades, Structure from Motion (SfM) and visual Simultaneous Localization and Mapping (visual SLAM) techniques have gained significant interest from both the computer vision and robotic communities. Many variants of these techniques have started to make an impact in a wide range of applications, including robot navigation and augmented reality. However, despite some remarkable results in these areas, most SfM and visual SLAM techniques operate based on the assumption that the observed environment is static. However, when faced with moving objects, overall system accuracy can be jeopardized. In this article, we present for the first time a survey of visual SLAM and SfM techniques that are targeted toward operation in dynamic environments. We identify three main problems: how to perform reconstruction (robust visual SLAM), how to segment and track dynamic objects, and how to achieve joint motion segmentation and reconstruction. Based on this categorization, we provide a comprehensive taxonomy of existing approaches. Finally, the advantages and disadvantages of each solution class are critically discussed from the perspective of practicality and robustness.
机译：在过去的几十年中，来自运动的结构（SfM）和可视化同时定位与制图（visual SLAM）技术引起了计算机视觉和机器人社区的极大兴趣。这些技术的许多变体已开始在包括机器人导航和增强现实在内的广泛应用中产生影响。但是，尽管在这些领域取得了显著成果，但大多数SfM和视觉SLAM技术仍基于观察到的环境是静态的假设进行操作。但是，当面对移动的物体时，可能会危及整个系统的准确性。在本文中，我们首次展示了针对动态环境中的操作的视觉SLAM和SfM技术的调查。我们确定了三个主要问题：如何执行重建（鲁棒的视觉SLAM），如何对动态对象进行分割和跟踪以及如何实现关节运动的分割和重建。基于此分类，我们提供了现有方法的全面分类法。最后，从实用性和鲁棒性的角度对每种解决方案类别的优缺点进行了严格的讨论。

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