Paper on RGB-D SLAM of Dr. ZHANG Yu's group accepted by TPAMI

"RGB-D SLAM in Dynamic Environments Using Point Correlations" (DSLAM), a paper of Dr. ZHANG Yu’s group has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). The work focuses on the simultaneous localization and mapping (SLAM) in dynamic environment. It is the first paper accepted by TPAMI whose first author affiliation is the College of Control Science and Engineering, Zhejiang University. Moreover, it is also the first SLAM paper accepted by TPAMI whose first author affiliation is domestic university or research institution.

In this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed. This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups. A sparse graph is first created from all map points. In this graph, the vertices represent map points, and each edge represents the correlation between adjacent points. If the relative position between two points remains consistent over time, there is correlation between them, and they are considered to be moving together rigidly. If not, they are considered to have no correlation and to be in separate groups. After the edges between the uncorrelated points are removed, the remaining graph separates the map points of moving objects from the static scene. The largest group is assumed to be the group of reliable static map points. Experimental results demonstrate that robust and accurate performance can be achieved by the proposed SLAM method in both slightly and highly dynamic environments. Compared to other state-of-the-art methods, the proposed method can provide competitive accuracy with good real-time performance.

To read the paper:

https://ieeexplore.ieee.org/document/9145704

Reporter: YU Hongxiang

Editor: WANG Jing