  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.
