In this paper we focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method uses as input the current pose of an agent (e.g. a robot) and a RGB-D frame. The task is to plan the next move that brings the agent closer to the target object. We model this problem as a Partially Observable Markov Decision Process solved by a Monte-Carlo planning approach. This allows us to make decisions on the next moves by iterating over the known scenario at hand, exploring the environment and searching for the object at the same time. Differently from the current state of the art in Reinforcement Learning, POMP does not require extensive and expensive (in time and computation) labelled data so being very agile in solving AVS in small and medium real scenarios. We only require the information of the floormap of the environment, an information usually available or that can be easily extracted from an a priori single exploration run. We validate our method on the publicly available AVD benchmark, achieving an average success rate of 0.76 with an average path length of 17.1, performing close to the state of the art but without any training needed. Additionally, we show experimentally the robustness of our method when the quality of the object detection goes from ideal to faulty.

中文翻译：

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POMP：基于 Pomcp 的在线运动规划，用于室内环境中的主动视觉搜索

在本文中，我们专注于通过在线设置学习已知室内环境中对象的主动视觉搜索 (AVS) 的最佳策略的问题。我们的 POMP 方法使用代理（例如机器人）的当前姿势和 RGB-D 框架作为输入。任务是计划使代理更接近目标对象的下一步行动。我们将此问题建模为通过蒙特卡洛规划方法解决的部分可观察马尔可夫决策过程。这使我们能够通过迭代手头的已知场景、探索环境并同时搜索对象来决定下一步行动。与当前强化学习领域的最新技术不同，POMP 不需要大量且昂贵的（在时间和计算上）标记数据，因此在中小型真实场景中解决 AVS 时非常灵活。我们只需要环境的平面图信息，一个通常可用的信息，或者可以很容易地从先验的单次探索运行中提取的信息。我们在公开可用的 AVD 基准测试中验证了我们的方法，平均成功率为 0.76，平均路径长度为 17.1，性能接近最先进，但无需任何培训。此外，当目标检测的质量从理想变为错误时，我们通过实验证明了我们方法的鲁棒性。表现接近最先进的水平，但无需任何培训。此外，当目标检测的质量从理想变为错误时，我们通过实验证明了我们方法的鲁棒性。表现接近最先进的水平，但无需任何培训。此外，当目标检测的质量从理想变为错误时，我们通过实验证明了我们方法的鲁棒性。