# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 1 Introduction  
There is a growing trend in the mining industry to develop towards big data, IoT, and robotization solutions. Considering the enormous amount of data collected from industrial automation, machinery, and equipment monitoring systems, the potential for the development of new technology to monitor mobile asset performance and technical conditions is very significant. Processing such an amount of data can be a task on its own. Very often, in the case of data collected on mobile assets, the factor limiting the scope of the analysis is the lack of spatial reference, which is crucial from the viewpoint of so-called awareness of operating context. This is especially important in the case of underground mines, where a multitude of different factors and variables can affect the collected measurements and in some cases--without a spatial context--can be very difficult to interpret in a commonly used data-driven approach. For example, the machine will enter a less ventilated area of the mine, which will result in exceeding the temperature data thresholds not related to its technical condition. In addition, having location methods tailored to the specific conditions of an underground mine, it is possible to develop methods of machine navigation, production optimization (e.g., indicating optimal haulage routes, mitigation of blockages), development of anti-collision systems, crisis management (indicating evacuation routes), or autonomous machines [1; 2; 3; 4; 5; 6; 7]. Obtaining information about the location of mobile vehicles is not a problem in the case of open-pit mines, thanks to the usage of Global Navigation Satellite Systems (GNSS), which of course cannot be utilized in an underground case [8]. Therefore, localization of mobile assets (i.e., LHD in the underground condition) is still an ongoing problem,especially in terms of autonomous haulage. As reported by [9], the first generation of Autonomously Guided Vehicles (AGVs) was developed in the 1960s, 1970s, and early 1980s. The principle of their operation was to follow wires buried in, or lines painted on, the floor. This type of navigation is called reactive navigation; the machine reacts to something in its immediate environment. The first methods of locating machines in underground mining excavation appeared in the 1990s. One of the first considerations that appears in conceptual work related to LHD automation has been shown in [10; 11]. The industrial applications were also focused on the teleoperation of LHDs during this period, which was less efficient in production than conventional manned systems [12; 13]. The author of [14] developed an approach based on the retroreflective stripe on the tunnel roof (back) detected by cameras to guide the vehicle. It was burdensome due to the constant need to change the reflector system with each change of haulage routes. Other work tested optical [15] and inductive detection [16]. However, in [17], it was proposed to use ultrasonic sensors to follow the walls of a tunnel. This approach was dedicated to planning activities, especially traffic management. Unfortunately, it did not fully meet the expectations because of the lack of an Earth-fixed coordinate system. In [18], the authors proposed the use of inertial sensors using a Kalman filter to integrate the signals. This approach, however, suffers from underutilization of data. Another article presents experimental work on LHD localization [19]. The proposed solution is based on explicit modeling of slip parameters as the vehicle maneuvers over rough terrain, and the use of inertial measurements to calculate these parameters. However, in [20], the authors proposed to install beacons to support the navigation system to locate LHD in underground mines. The method has already been tested in LKAB's Kiruna mine. Generally, between the 1990s and 2000s, absolute navigation developed strongly, in which the estimated absolute position is related to the fixed real-world coordinate system and known at all times [21; 22].