# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 1 Introduction
These methods required the combination of the machine's onboard monitoring system with external sensors. Their disadvantage is that the internal sensors (especially inertial, odometry, and heading angle) are subject to scale errors and offsets which lead to unbounded errors on the position estimate. Therefore, they must be corrected from time to time via the external absolute position measurement [9]. For outdoor conditions, correction can be made using GPS. On the other hand, in closed conditions, especially underground, the correction requires the use of detection of artificial beacons (e.g., retro-reflectors or radio transponder tags) or the use of natural features of the environment (e.g., unique excavation profiles when using a laser scanner). For this reason, the issue of mapping indoor environments became concurrent with the localization. You can refer to the chicken or the egg paradox. Hence, the new directions of absolute navigation were born, such as Simultaneous Localization and Map Building (SLAM) or Concurrent Mapping and Localization (CML). The topic also developed strongly in the 2000s in mobile robotics [23]. However, this did not stop the further development of reactive navigation [24; 25]. A preliminary model of the machine location without expanding the infrastructure is presented in [26]. All equipment used was embedded on the LHD. The data acquisition module consisted of a computer and articulation angle sensor, odometer, gyroscope, and two laser scanners. The method was based on the fusion of dead reckoning and position measurement using the natural features of the walls of the tunnels. The location using a gyroscope and an inclinometer in conjunction with a laser scanner for the perception of the environment is presented in [27]. In work on a test with a wheeled robot, [28] proposed an expanded node method according to the specific requirement for the path planning problem related to the unmanned underground LHD. The main purpose of the method is to adjust the characteristics of the LHD trajectory based on the estimated threat cost related to vehicle collision with the walls of the excavation. The literature review showed that most of the localization methods require the use of extensive sensors on the machine and in its surrounding, which significantly increases the cost of developing such a system and its further maintenance. There was a lack of solutions using only onboard monitoring, which is particularly cost-effective if the use of location is mainly dedicated to advanced analytics (e.g., predictive maintenance or production planning). To fill this gap, we propose an approach only based on inertial sensors built on the LHD, without the need to expand the infrastructure with external sensors and reference points. The work is an extension of [29], in which the authors proposed a localization method based on the correction of the vehicle motion path estimated by integrating the gyro signal with a digital map and the existing topology rules of the mine's road infrastructure. The approach is dedicated to room and pillar mining, where the excavation's structure has the form of a lattice (similar to Manhattan). Another addition was to integrate the detection of turns and machines passing through the intersection presented in [30], which can be treated as an auxiliary algorithm (tracking the passes through the subsequent intersection) and the correction of the gyro indications (recalculation of the angle assuming that the excavations intersect orthogonally). As an extension, in this article we offer an additional procedure of path correction which provides discrete information about natural features of the environment. Its task is to recognize whether a given vehicle at a specific moment in time was passing through a selected, specific part of the excavation, based on specific patterns appearing in the vibration signals recorded during travels with individual parts of the excavations.