Combining Simultaneous Localisation and Mapping (SLAM) estimation and dynamic scene modelling can highly benefit robot autonomy in dynamic environments. Robot path planning and obstacle avoidance tasks rely on accurate estimations of the motion of dynamic objects in the scene. This paper presents VDO-SLAM, a robust visual dynamic object-aware SLAM system that exploits semantic information to enable accurate motion estimation and tracking of dynamic rigid objects in the scene without any prior knowledge of the objects' shape or geometric models. The proposed approach identifies and tracks the dynamic objects and the static structure in the environment and integrates this information into a unified SLAM framework. This results in highly accurate estimates of the robot's trajectory and the full SE(3) motion of the objects as well as a spatiotemporal map of the environment. The system is able to extract linear velocity estimates from objects' SE(3) motion 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 and the results show consistent and substantial improvements over the state-of-the-art algorithms. An open-source version of the source code is available.
翻译:将同步定位和绘图(SLAM)和动态场景建模结合起来,对动态环境中的机器人自主性大有好处。机器人路径规划和障碍避免任务依赖于对动态物体在现场运动的准确估计。本文展示了VDO-SLAM,这是一个强大的视觉动态物体感知SLAM系统,它利用了语义信息,能够在不事先了解物体形状或几何模型的情况下对现场动态僵硬物体进行准确的动作估计和跟踪。拟议方法确定并跟踪环境中的动态物体和静态结构,并将这一信息纳入统一的SLAM框架。这导致对机器人的轨迹和整个SE(3)运动以及环境的波形图进行非常准确的估计。该系统能够从物体的SE(3)运动中提取直线速度估计,为复杂动态环境中的导航提供重要功能。我们展示了拟议系统在若干真正的室内和室外数据集上的性能,结果显示对最新算算法的一致和实质性改进。源代码的开源版本是可用的。