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

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

Blog Article

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 whole wheat phyllo dough is computationally low cost, allowing a 6.6 Hz execution rate.It obtains point cloud reconstruction g5210t-p90 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.