Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones

Abstract

Dynamic scene reconstruction in real environments is still an ongoing research challenge; moving objects affect the performance of static environment-based simultaneous localization and mapping and impede a correct scene reconstruction. This paper proposes a method for dynamic scene reconstruction using sensor fusion for dynamic simultaneous localization and mapping. It employs two-dimensional LIDAR statistical behaviour to detect and segment high variability point cloud areas containing a dynamic object. The method is computationally low cost, allowing a 6.6 Hz execution rate. It obtains point cloud reconstruction of a static scene by reducing, segmenting, and concatenating successive point clouds of a dynamic environment. The tests were in real indoor environments with a robotic vehicle and a person traversing a scene. The correlation between the static environment point cloud and successive reconstructed point clouds demonstrates that the proposed method reconstructs different environments in the presence of dynamic objects.