Computational techniques for locating industrial products in warehouses

Abstract

The computational estimation of an indoor or open-space warehouse inventory is based on the prior knowledge of the pallet dimensions. The input data consist of a three-dimensional point-cloud created by three- dimensional (3D) scanners (LiDAR technology) adapted to aerial ve- hicles (Drones). For research purposes, a storage simulator has been implemented in the Python language (version 3.9). In the rst phase, this research focuses on the ideal case of point-dispersion, with integer values for coordinates, in which a unit length corresponds to the distance between two neighboring pixels in the horizontal or vertical direction. In a subsequent stage, the generator of three-dimensional points will be modi ed to produce more realistic warehouse models. Improved ver- sions of existing algorithms will be proposed, taking into consideration the height variations.