  Current pandemic has caused the medical system to operate under high load. To
relieve it, robots with high autonomy can be used to effectively execute
contactless operations in hospitals and reduce cross-infection between medical
staff and patients. Although semantic Simultaneous Localization and Mapping
(SLAM) technology can improve the autonomy of robots, semantic object
association is still a problem that is worthy of being studied. The key to
solving this problem is to correctly associate multiple object measurements of
one object landmark by using semantic information, and to refine the pose of
object landmark in real time. To this end, we propose a hierarchical object
association strategy and a pose-refinement approach. The former one consists of
two levels, i.e., a short-term object association and a global one. In the
first level, we employ the multiple-object-tracking for short-term object
association, through which the incorrect association among objects whose
locations are close and appearances are similar can be avoided. Moreover, the
short-term object association can provide more abundant object appearance and
more robust estimation of object pose for the global object association in the
second level. To refine the object pose in the map, we develop an approach to
choose the optimal object pose from all object measurements associated with an
object landmark. The proposed method is comprehensively evaluated on seven
simulated hospital sequences1, a real hospital environment and the KITTI
dataset. Experimental results show that our method has an obviously improvement
in terms of robustness and accuracy for the object association and the
trajectory estimation in the semantic SLAM.
