# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 1 Introduction  
Locating robots and other vehicles has become a key topic in recent years in the Industry 4.0 framework [1]. Locating industrial vehicles, such as forklifts or laser-guided vehicles (LGVs), is critical to improve the management of large warehouses [2]. It was observed that the sequence in which products are picked and transported by forklifts may account for 55% of the total cost of warehouse operations [3, 4]. Therefore, optimizing this process through a continuous monitoring of forklift routes can contribute significantly to the plant efficiency. Additionally, localization of forklifts or other vehicles is the first necessary requirement to implement autonomous vehicles [5]. As field practitioners are now aware, the lack of a reliable Global Navigation Satellite System (GNSS) signal in indoor environments, such as industrial plants and warehouses, makes it necessary to develop localization solutions based on alternative technologies [6]. In many applications, the vehicle coordinates, together with the vehicle orientation, i.e., the vehicle pose, must be estimated [7]. A vehicle can be theoretically localized through _proproieceptive sensors_ that measure its ego-motion parameters, such as inertial measurement units (IMUs) [8], rotary encoders [9], or optical flow sensors [10, 11], through a _dead reckoning_ process [12]. However, such a solution is not applicable in the long-term, as the nature of these sensors leads to an accumulation of localization error and a consequent drift of the estimated trajectory. Therefore, to bind the localization uncertainty, _proprioceptive sensor_ data are usually merged with _exteroceptive sensor_ data, providing information about the surrounding environment [13, 14]. Several technologies exist that can be adopted as providers of external information in the sound [15, 16, 17], optical [18, 19, 20, 21, 22, 23], and electromagnetic domains [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]. The sound domain is particularly suitable for performing vehicle localization through rotating sonars equipped on the vehicle [17], but it suffers from several problems, such as noise pollution, which could be significant in industrial environments. Particularly interesting is the optical domain, which is widely represented in the state-of-the-art [19; 20; 21; 22; 23]. The optical domain concerns both solutions with 2D infrared [19] and 3D rotating Light Detection And Ranging (LiDAR) [20] or computer vision (CV) techniques in the visible light domain [21; 22; 23]. The first type of solutions can achieve accurate localization performance thanks to the recent improvements in localization and simultaneous localization and mapping (SLAM) algorithms for mobile robots [19]. Rotating LiDARs are able to reconstruct the environment map and to measure the trajectory of the vehicle contextually, but they suffer from the problem of moving obstacles in their field of view, which may cause the map-creation process to fail. Therefore, a crowded or dynamic environment could not be suitable for these systems. The CV techniques based on visible-light cameras are robust, but indeed suffer similar issues. Moreover, the computational burden of these methods is high; poor light conditions may invalidate the quality of the gathered images, and privacy issues can be raised if the camera gets images/videos of people. Some technologies in the electromagnetic domain, instead, provide a more robust way to release from the environmental effects. For example, if the area to be monitored is small, magnetic systems can be used [24; 25; 26]. Magnetic fields are also useful in vehicle localization for magnetometers contained in IMUs, used to calibrate gyroscope biases and to improve the robot tracking performance [27]. Radio frequency (RF) technologies are more suitable for several distance-measurement strategies depending on the available bandwidth.