# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 1 Introduction  
Forklifts are one of the most popular types of handling equipment on the market [1], and the demand for them tends to increase year by year [2], because they offer many benefits, such as improved productivity and reduced manual handling [3]. The Research and Markets research agency predicts that the forklift market will record sales of about 2.2 million units in 2023, with a compound annual growth rate of about 9%. According to a report by the PR Newswire agency, the forklift market will grow at a rate of 7.8% annually, increasing from 2 billion USD in 2020 USD to 2.9 billion in 2035 [4]. The application of such a large number of forklifts has and will result in their involvement with high accident and fatality rates around the world [5; 6; 7]. Researchers have reported that most accidents can be attributed to human operator errors, such as a lack of attention, misperception, or misjudgment [8; 9]. The US Occupational Safety and Health Administration (OSHA) also reported that a significant number of forklift accident cases are caused by reduced situation awareness of the forklift operator [10; 11]. The prevalence of this type of accident clearly highlights the importance of developing automatic-drive forklifts. The use of automatic-drive forklifts still requires the development of key technologies for tasks such as autonomous navigation, pallet location, and pallet recognition. Automated guided vehicles (AGVs) have been proposed since the early 1950s, and the first AGV was created in America [12; 13] to help improve the autonomous navigation of forklifts. Researchers have intensely studied optimal control theories and optimization methods for solving AGV path planning and navigation problems in recent years [14; 15; 16; 17]. [PERSON] et al. [15] proposed a learning-based algorithm to solve global path planning problems, which involved training a deep convolutional neural network with dual branches (DB-CNN). However, it is more difficult to recognize and locate pallets in actual warehouses because pallets are placed with high uncertainty. To address this issue, the authors of [18; 19] defined a semi-structured environment representing work conditions that contained information about pallets and the environment as a priori information, including the desired pose, goods loaded on pallets, and goods on a shelf or the ground. The present investigation aimed to address three key issues that must be overcome in semi-structured environments. First, a forklift algorithm has to identify pallets with uncertainty and subsequently maintain a continuously accurate estimation of the pose parameters while approaching and picking up pallets [19]. The pose of a target pallet cannot be predetermined due to errors in human operation. Second, a forklift algorithm should identify various pallets because several types of pallets and goods are used in warehouses. Third, the algorithm should have the ability to segment multiple pallets. Segmenting pallets situated within a small distance of each other is difficult. If the pallet detection algorithm cannot overcome these problems, then the fork may damage goods and lead to security incidents. Some researchers have explored various algorithms for pallet recognition based on a single sensor. An algorithm based on calculating geometry using pallet edges and shape features was investigated, but the features could be incorrectly extracted when the pallet was blocked or the illumination conditions changed. In [19; 20], the pose of single pallets was estimated based on geometry features extracted from distance data provided by LiDAR with geometry classifiers. In addition, the authors of [21; 22; 23; 24; 25; 26] obtained pallet pose information based on pallet size and edge features extracted from a color image provided by a camera. To reduce the effect of the environment, pallets were identified with marks attached to the pallet feet in [27; 28; 29]. However, the processes of adding and calibrating the mark were found to lead to higher costs and more human work.