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In this paper, a simultaneous localization and mapping (SLAM) method thateliminates the influence of moving objects in dynamic environments is proposed.This method utilizes the correlation between map points to separate points thatare part of the static scene and points that are part of different movingobjects into different groups. A sparse graph is first created using Delaunaytriangulation from all map points. In this graph, the vertices represent mappoints, and each edge represents the correlation between adjacent points. Ifthe relative position between two points remains consistent over time, there iscorrelation between them, and they are considered to be moving togetherrigidly. If not, they are considered to have no correlation and to be inseparate groups. After the edges between the uncorrelated points are removedduring point-correlation optimization, the remaining graph separates the mappoints of the moving objects from the map points of the static scene. Thelargest group is assumed to be the group of reliable static map points.Finally, motion estimation is performed using only these points. The proposedmethod was implemented for RGB-D sensors, evaluated with a public RGB-Dbenchmark, and tested in several additional challenging environments. Theexperimental results demonstrate that robust and accurate performance can beachieved by the proposed SLAM method in both slightly and highly dynamicenvironments. Compared with other state-of-the-art methods, the proposed methodcan provide competitive accuracy with good real-time performance.

RGB-D SLAM in Dynamic Environments Using Point Correlations