This paper addresses the problem of enabling a robot to search for a semantic object in an unknown and GPS-denied environment. For the robot in the unknown environment to detect and find the target 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 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 planning. The results demonstrate the effectiveness and efficiency of our approach.

翻译：本文解决了机器人在未知且无GPS环境中搜索语义目标的问题。为使未知环境中的机器人能够检测并发现目标物体，机器人必须利用其机载传感器在几何和语义两个层面同时进行同步定位与地图构建（SLAM），同时基于不断更新的SLAM结果规划并执行运动。换言之，机器人必须在存在感知和运动不确定性的情况下，实时完成同步定位、语义建图、运动规划与执行。这是一个开放性难题，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，机器人的运动目标会随其能否检测到目标物体以及检测方式而动态变化。我们提出了一种新颖的方法来解决该问题，该方法融合了语义SLAM、贝叶斯网络、马尔可夫决策过程以及实时动态规划。实验结果证明了我们方法的有效性和高效性。