M.Ing. (Electrical and Electronic Engineering) / This dissertation deals with the mapping of an unknown environment. Mapping of an environment can be accomplished by asking the question “What is in my world?” whilst moving through the environment. Once the objects occupying the ‘world’ have been discovered, the locations of these objects are stored somewhere (for example on paper), so that the environment can be navigated at a later stage. In the context of robots, a map provides the robot with a certain degree of “intelligence”. Several different types of applications are available for robots with “intelligence”; ranging from mining applications, to search and rescue situations, to surveillance applications and recognisance applications. The research hypothesis posed by this dissertation is as follows: Produce a human readable map for an unknown defined structured environment using a single laser range finder (LRF). The focus was on mapping environments resembling mine tunnels. In mine tunnel environments sensors, such as wheel odometers, can fail. This failure makes it advantageous to be able to create a map of the environment with the data obtained solely from the LRF. For this dissertation, the following restrictions were placed on the environment being mapped. It had to be structured (i.e. the environment could be described by simple geometric primitives such as lines); it had to be static (the only entity allowed to move in the environment was the LRF to obtain data); and the environment had to be defined (i.e. have a starting and ending point). During the course of this Masters research, it was discovered that in order to create a human readable map, one has to determine the accurate localisation of the sensor in the environment whilst mapping. The described scenario is a typical problem in mapping and is referred to as the ‘simultaneous localisation and mapping (SLAM) problem’. This dissertation shows results when mapping was done with – and without – accurate localisation. The final approach used to create the human readable map consisted of determining scan matched odometry (based on a feature matching and ICP algorithm). The scan matched odometry is incorporated into a grid-based SLAM technique that utilises a particle filter to accurately determine the position of the sensor in the environment, in order to create a human readable map of the environment. The algorithm used (as described) was able to close loops (i.e. the mapping algorithm was able to handle the sensor returning to its starting point) and it produced satisfactory results for the types of environments as required by the scope of this dissertation.

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