object recognition

The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.

Progress in the areas of 3D sensor systems and artificial intelligence provides new opportunities for the development of flexible robotic systems that are applicable in scenarios without predefined and constant environmental conditions or standardized processes. An example from the field of logistics is the automatic unloading of containers. The development of a suitable robotic system on the one hand requires applicable gripping technologies, on the other hand it requires a reliable object recognition method in order to recognize and localize differently shaped logistic goods within a packaging scenario. This paper presents an object recognition method for logistic goods from three different shape classes using point clouds acquired by a laser scanner. The method is evaluated with real packaging scenarios.

There are numerous and relevant application domains like inspection, maintenance, surveillance, and handling of non-uniform goods where the use of robots would be beneficial. This article addresses the reasons that make it difficult for a present-day robot to master these domains. The dual arm system AILA will be presented, which takes further steps in the direction of such a use case.