  Nowadays in the field of semantic SLAM, how to correctly use semantic
information for data association is still a problem worthy of study. The key to
solving this problem is to correctly associate multiple object measurements of
one object landmark, and refine the pose of object landmark. However, different
objects locating closely are prone to be associated as one object landmark, and
it is difficult to pick up a best pose from multiple object measurements
associated with one object landmark. To tackle these problems, we propose a
hierarchical object association strategy by means of multiple object tracking,
through which closing objects will be correctly associated to different object
landmarks, and an approach to refine the pose of object landmark from multiple
object measurements. The proposed method is evaluated on a simulated sequence
and several sequences in the Kitti dataset. Experimental results show a very
impressive improvement with respect to the traditional SLAM and the
state-of-the-art semantic SLAM method.
