  It is often desirable to capture and map semantic information of an
environment during simultaneous localization and mapping (SLAM). Such semantic
information can enable a robot to better distinguish places with similar
low-level geometric and visual features and perform high-level tasks that use
semantic information about objects to be manipulated and environments to be
navigated. While semantic SLAM has gained increasing attention, there is little
research on semanticlevel data association based on semantic objects, i.e.,
object-level data association. In this paper, we propose a novel object-level
data association algorithm based on bag of words algorithm, formulated as a
maximum weighted bipartite matching problem. With object-level data association
solved, we develop a quadratic-programming-based semantic object initialization
scheme using dual quadric and introduce additional constraints to improve the
success rate of object initialization. The integrated semantic-level SLAM
system can achieve high-accuracy object-level data association and real-time
semantic mapping as demonstrated in the experiments. The online semantic map
building and semantic-level localization capabilities facilitate semantic-level
mapping and task planning in a priori unknown environment.
