The unscented Kalman filter (UKF) is a method to solve nonlinear dynamic filtering problems, which internally uses the unscented transform (UT). The behavior of the UT is controlled by design parameters, seldom changed from the values suggested in early UT/UKF publications. Despite the knowledge that the UKF can perform poorly when the parameters are improperly chosen, there exist no wide spread intuitive guidelines for how to tune them. With an application relevant example, this paper shows that standard parameter values can be far from optimal. By analyzing how each parameter affects the resulting UT estimate, guidelines for how the parameter values should be chosen are developed. The guidelines are verified both in simulations and on real data collected in an underground mine. A strategy to automatically tune the parameters in a state estimation setting is presented, resulting in parameter values inline with developed guidelines.

Lidar-based positioning in a 2D map is analyzed as a method to provide a robust, high accuracy, and infrastructure-free positioning system required by the automation development in underground mining. Expressions are derived that highlight separate information contributions to the obtained position accuracy. This is used to develop two new methods that efficiently select which subset of available lidar rays to use to reduce the computational complexity and allow for online processing with minimal loss of accuracy. The results are verified in simulations of a mid-articulated underground loader in a mine. The methods are shown to be able to reduce the number of rays needed without considerably affecting the performance, and to be competitive with currently used methods. Furthermore, simulations highlight the effects of errors in the map and other map properties, and how imperfect maps degrades the performance of different selection strategies.

Robust and highly accurate position estimation in underground mines is investigated by considering a vehicle equipped with 2D laser scanners. A survey of available methods to process data from such sensors is performed with focus on feature extraction methods. Pros and cons of the usage of different methods for the positioning application with 2D laser data are highlighted, and suitable methods are identified. Three state-of-the-art feature extraction methods are adapted to the scenario of positioning in a predefined map and the methods are evaluated through experiments conducted in a simulated underground mine environment. Results indicate that feature extraction methods perform in parity with the method of matching each ray individually to the map, and better than the point cloud scan matching method of a pure ICP, assuming a highly accurate map is available. Furthermore, experiments show that feature extraction methods more robustly handle imperfections or regions of errors in the map by automatically disregarding these regions.

Long-term autonomy of robots requires localization in an inevitably changing environment, where the robots' knowledge about the surroundings are more or less uncertain. Inspired by methods in target tracking, this paper proposes a feature based multi-hypothesis map representation to provide robust localization under these conditions. It is derived how this representation can be used to obtain consistent position estimates while at the same time providing up-to-date map information to be shared by cooperative robots or for visual presentation. Simulations are performed that conceptually highlights the benefit of the developed solution in an environment where uniquely identifiable landmarks are moved between discrete positions. This relates to a real world scenario where a robot moves in a corridor with office doors opened or closed at different times. 

A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not valid. This paper studies a scenario where uniquely identifiable landmarks can attend multiple discrete positions, not known a priori . Based on a feature based multi-hypothesis map representation, a multi-hypothesis SLAM algorithm is developed inspired by target tracking theory. The creation of such a map is merged into the SLAM framework allowing any available SLAM method to solve the underlying mapping and localization problem for each hypothesis. A recursively updated hypothesis score allows for hypothesis rejection and prevents exponential growth in the number of hypotheses. The developed method is evaluated in an underground mine application, where physical barriers can be moved in between multiple distinct positions. Simulations are conducted in this environment showing the benefits of the multi-hypothesis approach compared to executing a standard SLAM algorithm. Practical considerations as well as suitable approximations are elaborated upon and experiments on real data further validates the simulated results and show that the multi-hypothesis approach has similar performance in reality as in simulation.