  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 using Delaunay
triangulation 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
during point-correlation optimization, the remaining graph separates the map
points of the moving objects from the map points of the static scene. The
largest group is assumed to be the group of reliable static map points.
Finally, motion estimation is performed using only these points. The proposed
method was implemented for RGB-D sensors, evaluated with a public RGB-D
benchmark, and tested in several additional challenging environments. The
experimental results demonstrate that robust and accurate performance can be
achieved by the proposed SLAM method in both slightly and highly dynamic
environments. Compared with other state-of-the-art methods, the proposed method
can provide competitive accuracy with good real-time performance.
