Simultaneously Search and Localize Semantic Objects in Unknown Environments

For a robot in an unknown environment to find a 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 simultaneously conduct localization, semantic mapping, motion planning, and execution online in the presence of sensing and motion uncertainty. This is an open problem as it intertwines semantic SLAM and adaptive online motion planning and execution under uncertainty based on perception. Moreover, the goals of the robot's 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, and online probabilistic motion planning. The results demonstrate our approach's effectiveness and efficiency.