Obtaining and Monitoring Warehouse 3D Models with Laser Scanner Data

Abstract

This paper is focused on creating semantic 3D models of unstructured warehouses from coloured point clouds. Several scans from different locations of a laser scanner are integrated into a unique 3D dataset that is afterwards processed. The paper presents an efficient 3D processing algorithm that is able to segment, recognize and locate the existing materials of a storage place. The obtained 3D model provides to logistic managers precise and valuable information, such as: the current location of the stock, the free space for coming merchandises or the occupied volume variations between two scanning sessions taken at different times. The method has been tested under noise conditions in simulated scenarios and the extracted model has been compared with a ground truth model. The good results demonstrate that this approach could be useful in the logistic field.