This paper addresses the topic of semantic world modeling by conjoining probabilistic reasoning and object anchoring. The proposed approach uses a so-called bottom-up object anchoring method that relies on rich continuous attribute values measured from perceptual sensor data. A novel anchoring matching function learns to maintain object entities in space and time and is validated using a large set of trained humanly annotated ground truth data of real-world objects. For more complex scenarios, a high-level probabilistic object tracker has been integrated with the anchoring framework and handles the tracking of occluded objects via reasoning about the state of unobserved objects. We demonstrate the performance of our integrated approach through scenarios such as the shell game scenario, where we illustrate how anchored objects are retained by preserving relations through probabilistic reasoning.

中文翻译：

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语义关系对象跟踪

本文通过结合概率推理和对象锚定来解决语义世界建模的主题。所提出的方法使用所谓的自下而上的对象锚定方法，该方法依赖于从感知传感器数据中测量的丰富的连续属性值。一种新颖的锚定匹配函数学习在空间和时间上维护对象实体，并使用大量经过训练的、经过人工注释的真实世界对象的真实数据进行验证。对于更复杂的场景，高级概率对象跟踪器已与锚定框架集成，并通过推理未观察到的对象的状态来处理被遮挡对象的跟踪。我们通过 shell 游戏场景等场景展示了我们的集成方法的性能，