Pallet recognition and localization using an RGB-D camera

Abstract

This article reports our research results on an autonomous forklift, with the focus on pallet recognition and localization using an RGB-D camera. It is a fundamental issue for unmanned storehouses, which enables the forklift to insert the forks within the pallet’s slots for loading and unloading packages. Particularly, a pallet recognition and localization approach is presented. The range image is firstly segmented into planar patches based on a region growing algorithm. Then, the segments are filtered heuristically according to the storehouse environment. Afterward, a template matching method is utilized to recognize pallets in the remained segments, based on calculating the degree of similarity at each location during sliding the templates on the segment. Once a pallet has been recognized, its pose is calculated straightforward. The article has three main contributions, that is, a low-cost RGB-D camera is employed for pallet recognition and localization, where only depth information has been utilized; using the proposed method, multiple kinds of pallets can be used at the same time, which provides a flexibility for the storehouse; and furthermore, the method has a good expansibility to allow the storehouse to adopt new pallets easily.