RGB-D simultaneous localization and mapping based on combination of static point and line features in dynamic environments.

Visual simultaneous localization and mapping (SLAM) based on RGB-D data has been extensively researched in the past few years and has many robotic applications. Most of the state-of-the-art approaches assume static environments. However, the static assumption is not usually true in real world environments, dynamic objects can severely degrade the SLAM performance. In order to reduce the influence of dynamic objects on camera pose estimation, this paper proposes an approach that uses static point and line features. Static weights of point and line features indicating the likelihood of features being part of static environment are estimated. According to the calculated static weights, the data associated with dynamic objects are filtered out. The remaining static point and line features are considered inputs for refined pose estimation. Experiments are conducted with challenging dynamic sequences from TUM RGB-D dataset. The results demonstrate that the proposed approach is able to effectively improve the accuracy of RGB-D SLAM in dynamic environments. [ABSTRACT FROM AUTHOR]