The scene rigidity assumption, also known as the static world assumption, is common in SLAM algorithms. Most existing algorithms operating in complex dynamic environments simplify the problem by removing moving objects from consideration or tracking them separately. Such strong assumptions limit the deployment of autonomous mobile robotic systems in a wide range of important real world applications involving highly dynamic and unstructured environments. This paper presents VDO-SLAM, a robust object-aware dynamic SLAM system that exploits semantic information to enable motion estimation of rigid objects in the scene without any prior knowledge of the objects shape or motion models. The proposed approach integrates dynamic and static structures in the environment into a unified estimation framework resulting in accurate robot pose and spatio-temporal map estimation. We provide a way to extract velocity estimates from object pose change of moving objects in the scene providing an important functionality for navigation in complex dynamic environments. We demonstrate the performance of the proposed system on a number of real indoor and outdoor datasets. Results show consistent and substantial improvements over state-of-the-art algorithms. An open-source version of the source code is available.

中文翻译：

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VDO-SLAM：视觉动态对象感知 SLAM 系统

场景刚性假设，也称为静态世界假设，在 SLAM 算法中很常见。大多数在复杂动态环境中运行的现有算法通过从考虑中移除移动对象或单独跟踪它们来简化问题。这种强大的假设限制了自主移动机器人系统在涉及高度动态和非结构化环境的广泛重要的现实世界应用中的部署。本文介绍了 VDO-SLAM，这是一种强大的物体感知动态 SLAM 系统，它利用语义信息来实现场景中刚性物体的运动估计，而无需任何物体形状或运动模型的先验知识。所提出的方法将环境中的动态和静态结构整合到一个统一的估计框架中，从而实现准确的机器人姿态和时空地图估计。我们提供了一种从场景中移动物体的物体姿态变化中提取速度估计值的方法，为复杂动态环境中的导航提供了重要的功能。我们在许多真实的室内和室外数据集上展示了所提出系统的性能。结果表明，与最先进的算法相比，取得了一致且实质性的改进。提供了源代码的开源版本。我们在许多真实的室内和室外数据集上展示了所提出系统的性能。结果表明，与最先进的算法相比，取得了一致且实质性的改进。提供了源代码的开源版本。我们在许多真实的室内和室外数据集上展示了所提出系统的性能。结果表明，与最先进的算法相比，取得了一致且实质性的改进。提供了源代码的开源版本。