In this work, we present a method for tracking and learning the dynamics of all objects in a large scale robot environment. A mobile robot patrols the environment and visits the different locations one by one. Movable objects are discovered by change detection, and tracked throughout the robot deployment. For tracking, we extend the Rao-Blackwellized particle filter of previous work with birth and death processes, enabling the method to handle an arbitrary number of objects. Target births and associations are sampled using Gibbs sampling. The parameters of the system are then learnt using the Expectation Maximization algorithm in an unsupervised fashion. The system therefore enables learning of the dynamics of one particular environment, and of its objects. The algorithm is evaluated on data collected autonomously by a mobile robot in an office environment during a real-world deployment. We show that the algorithm automatically identifies and tracks the moving objects within 3D maps and infers plausible dynamics models, significantly decreasing the modeling bias of our previous work. The proposed method represents an improvement over previous methods for environment dynamics learning as it allows for learning of fine grained processes.
翻译：在这项工作中,我们提出了一个在大型机器人环境中跟踪和学习所有物体动态的方法。 一个移动机器人在环境中巡逻,逐个访问不同地点。 通过变化探测发现可移动物体,并在整个机器人部署过程中跟踪。为了跟踪,我们扩展了先前工作与出生和死亡过程的Rao-Breakwellized粒子过滤器,使处理任意数量物体的方法得以使用Gibbs取样方法。目标出生和关联。然后使用未受监督的方式使用期望最大化算法来学习系统的参数。因此,该系统能够了解特定环境及其对象的动态。在实际部署期间,对由移动机器人在办公室环境中自动收集的数据进行评估。我们显示算法自动识别和跟踪3D地图中的移动物体,并推断出合理的动态模型,大大缩小了我们先前工作的模型偏差。拟议方法表明,在学习精细粒过程时,比以往的环境动态学习方法有所改进。