Pallets are intensely used in the warehouses and retailing stores and the automation of pallet localization and detection are highly desired and studied for forklift robot and pallet-picking instruments. Due to the fact that pallet types are varied a lot in practice, it’s extremely difficult to develop single solution to detect all types of pallet. This article presents a general review of pallet identification and localization techniques for industrial forklift robot and pallet-picking instrument. Some modern computer-vision techniques are reviewed and compared. In particular, Deep Neural Network (DNN) method is usually applied to detect and locate the pallet in the RGB images. The Point Cloud method is used to label region of interest (RoI) in 2D range data and the pallet’s feature is extracted and this method is able to provide the precise localization of the pallets. Here, Pallet identification and localization algorithm (PILA) strategy is introduced and this approach could deliver highly-precise orientation angle and centric location of the pallets without any artificial assistance, which utilizes RGB image and Point-cloud data to balance the localization precision and running-time with low-cost hardware. The experimental results show that the pallet could be located with the 3D localization accuracy of 1cm and angle resolution of 0.4 degree at the distance of 3m with running time less than 700ms. PILA is a promising solution for autonomous pallet picking instrument and self-driving forklift applications.

Keywords: Pallet Recognition; Pallet Localization; Deep Neutral Network; RGBD camera