Abstract
The mining sector is one of the most promising areas for implementing advanced autonomous robots. The benefits of increased safety, robot actions’ repeatability, and reducing human presence in hazardous locations are especially important in underground mines. One of the core functionalities of such a device is the robot’s ability to localize and navigate itself in the working environment. To achieve this, simultaneous localization and mapping (SLAM) techniques are used. In selected cases, they also allow the acquisition of dense spatial data in the form of 3D point clouds, which can be utilized for various 3D modeling and spatial analysis purposes. In this work, a mobile robot, equipped only with a compact laser scanner, is used to acquire spatial data in the adit of a closed mine in Zloty Stok, Poland. This data is further processed with selected SLAM algorithms to create a homogeneous 3D point cloud. Results are visualized and compared to a model obtained with a survey-grade laser scanner. Accuracy evaluation shows that employing SLAM algorithms to process data collected by a mobile robot can produce a reasonably accurate 3D geometrical model of an underground tunnel, even without incorporating any additional sensors.