The mining industry is currently facing a transition from manually operated vehicles to remote or semi-automated vehicles. The vision is fully autonomous vehicles being part of a larger fleet, with humans only setting high-level goals for the autonomous fleet to execute in an optimal way. An enabler for this vision is the presence of robust, reliable and highly accurate localization. This is a requirement for having areas in a mine with mixed autonomous vehicles, manually operated vehicles, and unprotected personnel. The robustness of the system is important from a safety as well as a productivity perspective. When every vehicle in the fleet is connected, an uncertain position of one vehicle can result in the whole fleet begin halted for safety reasons.

Providing reliable positions is not trivial in underground mine environments, where access to global satellite based navigation systems is denied. Due to the harsh and dynamically changing environment, onboard positioning solutions are preferred over systems utilizing external infrastructure. The focus of this thesis is localization systems relying only on sensors mounted on the vehicle, e.g., odometers, inertial measurement units, and 2D LIDAR sensors. The localization methods are based on the Bayesian filtering framework and estimate the distribution of the position in the reference frame of a predefined map covering the operation area. This thesis presents research where the properties of 2D LIDAR data, and specifically characteristics when obtained in an underground mine, are considered to produce position estimates that are robust, reliable, and accurate.

First, guidelines are provided for how to tune the design parameters associated with the unscented Kalman filter (UKF). The UKF is an algorithm designed for nonlinear dynamical systems, applicable to this particular positioning problem. There exists no general guidelines for how to choose the parameter values, and using the standard values suggested in the literature result in unreliable estimates in the considered application. Results show that a proper parameter setup substantially improves the performance of this algorithm.

Next, strategies are developed to use only a subset of available measurements without losing quality in the position estimates. LIDAR sensors typically produce large amounts of data, and demanding real-time positioning information limits how much data the system can process. By analyzing the information contribution from each individual laser ray in a complete LIDAR scan, a subset is selected by maximizing the information content. It is shown how 80% of available LIDAR measurements can be dropped without significant loss of accuracy.

Last, the problem of robustness in non-static environments is addressed. By extracting features from the LIDAR data, a computationally tractable localization method, resilient to errors in the map, is obtained. Moving objects, and tunnels being extended or closed, result in a map not corresponding to the LIDAR observations. State-of-the-art feature extraction methods for 2D LIDAR data are identified, and a localization algorithm is defined where features found in LIDAR data are matched to features extracted from the map. Experiments show that regions of the map containing errors are automatically ignored since no matching features are found in the LIDAR data, resulting in more robust position estimates.