未知环境中语义目标的自主搜索
Autonomous Search of Semantic Objects in Unknown Environments
AI总结：
本文解决机器人在无GPS未知环境中搜索语义目标的问题，提出结合语义SLAM、贝叶斯网络、马尔可夫决策过程和实时动态规划的新方法，实现实时定位、建图与运动规划，并通过仿真和真实实验验证了其有效性与高效性。
摘要：
本文探讨了如何让机器人在未知且无GPS信号的环境中搜索语义目标（即带有语义标签的物体）的问题。为了让处于未知环境中的机器人能够检测并找到目标语义物体，它必须利用其机载传感器在几何和语义两个层面上同时进行定位与建图（SLAM），同时基于不断更新的SLAM结果来规划并执行其运动。换言之，机器人必须能够在存在感知和运动不确定性的情况下，实时进行同步定位、语义建图、运动规划和执行。这是一个开放性问题，因为它结合了基于感知的语义SLAM以及不确定性下的实时运动规划与执行。此外，机器人运动的目标会根据其能否以及如何检测到目标物体而动态改变。我们提出了一种新颖的方法来解决该问题，该方法利用了语义SLAM、贝叶斯网络、马尔可夫决策过程和实时动态规划。仿真和真实实验的结果证明了我们方法的有效性和高效性。
英文摘要：
This paper addresses the problem of enabling a robot to search for a semantic object, i.e., an object with a semantic label, in an unknown and GPS-denied environment. For the robot in the unknown environment to detect and find the target semantic object, it must perform simultaneous localization and mapping (SLAM) at both geometric and semantic levels using its onboard sensors while planning and executing its motion based on the ever-updated SLAM results. In other words, the robot must be able to conduct simultaneous localization, semantic mapping, motion planning, and execution in real-time in the presence of sensing and motion uncertainty. This is an open problem as it combines semantic SLAM based on perception and real-time motion planning and execution under uncertainty. Moreover, the goals of the robot motion change on the fly depending on whether and how the robot can detect the target object. We propose a novel approach to tackle the problem, leveraging semantic SLAM, Bayesian Networks, Markov Decision Process, and Real-Time Dynamic Programming. The results in simulation and real experiments demonstrate the effectiveness and efficiency of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
