A Hierarchical Approach to Active Semantic Mapping Using Probabilistic Logic and Information Reward POMDPs
A Hierarchical Approach to Active Semantic Mapping Using Probabilistic Logic and Information Reward POMDPs
复制标题
使用概率逻辑和信息奖励 POMDP 的主动语义映射的分层方法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
P. Lima
中科院分区：
文献类型：
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作者：
Tiago Veiga;Miguel Silva;R. Ventura;P. Lima
维护复杂动态环境的语义图是一个具有挑战性的问题，其中不确定性源于嘈杂的感知和意外的变化。我们特别关注移动代理维护环境语义地图的问题。在本文中，我们以分层的方式解决这个问题。首先，我们使用一个概率逻辑模型来表示语义映射，以及相关的不确定性。其次，我们用一组信息奖励POMDP模型来模拟机器人与环境的交互，每个模型对应一个环境分区（例如，一个房间）。执行分区是为了解决POMDP模型在非常大的状态空间上的可伸缩性限制。然后，我们使用概率推理来确定接下来执行哪个POMDP和策略。实验结果表明，该架构在真实家政机器人场景下是有效的。
Maintaining a semantic map of a complex and dynamic environment, where the uncertainty originates in both noisy perception and unexpected changes, is a challenging problem. In particular, we focus on the problem of maintaining a semantic map of an environment by a mobile agent. In this paper we address this problem in an hierarchical fashion. Firstly, we employ a probabilistic logic model representing the semantic map, as well as the associated uncertainty. Secondly, we model the interaction of the robot with the environment with a set of information-reward POMDP models, one for each partition of the environment (e.g., a room). The partition is performed in order to address the scalability limitations of POMDP models over very large state spaces. We then use probabilistic inference to determine which POMDP and policy to execute next. Experimental results show the efficiency of this architecture in real domestic service robotic scenarios.