LAUSR

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. Note to Practitioners—Robust positioning in GNSS denied environments is a complex and real problem experienced by practitioners in many fields. The focus of this paper is underground mining; however, the findings and discussions have bearing on many other applications where reliable GNSS is unavailable and lidar is used for positioning in unstructured environments. An important take home message, of interest in any application with dynamically changing environments, is that by using feature extraction map errors are automatically handled as features are simply not matched in erroneous regions. The experiments in this paper are performed with a realistic laser simulation model trained on real data; hence the gap between simulation and reality is relatively small ensuring the results are relevant for practical purposes. In general, the feature extraction methods are sensitive to the parameter settings, and would have to be properly tuned for the specific application. Furthermore, the computational complexity, which is only mentioned briefly in this paper, varies a lot between the methods and has to be investigated further to ensure meeting real-time requirements. Since the feature extraction methods robustly handle errors in the map, future research will explore how this can be used to enable automatic updates of the map.