Many robotic systems rely on laser ranging sensors to navigate and map their environment. As robots are getting more advanced, smaller and cheaper, their sensors need to decrease in size and cost, while increasing their reliability and accuracy. Current laser-based sensors have difficulty to meet these properties. They are either large, expensive, contain moving parts or provide insufficient amounts of information. This thesis introduces an integrated approach for the development of an eye-safe, low-cost, solid-state, wide-angle line-laser distance sensor, tackling these challenges. A prototype is derived by constructing a generic, camera independent, triangulation model. The model, combined with a set of pre-defined requirements, is used to select hardware components and predict the sensor limits. A unified calibration step is proposed to estimate both camera intrinsics as well as misalignment errors with the same set of data. Additionally, a microsecond-accurate open loop synchronization system for laser activation and imager exposure is presented. Tests show that the prototyped sensor is able to measure distances up to 3 meters with an error of 4%. At 2 meter, the error is just under 2%. Additionally, the solid-state prototype has a field-of-view of 105 degrees, an angular resolution of 0.8 degrees, an update rate of 10Hz and an estimated cost of just below $35.

A fundamental prerequisite for many robot-tasks is the availability of an environment model. The ability of a robot to create such a model itself is crucial to having truly autonomous robots around in our daily lives. For basic robot-tasks, a two-dimensional grid-map that describes a distinction between inhabitable and uninhabitable space is a commonly used model. Creating grid-maps, or any environment model, is usually done by simultaneously estimating the robots location and its environment. Modern SLAM algorithms are based on incremental non-linear optimization and the robustness highly depends on the used sensors and their proneness to environmental ambiguities. In this thesis an affordable laser-range-finder (often referred to as LiDAR is used in combination with ultra-wideband radios to improve the localization and mapping performance in large ambiguous environments. In SLAM-literature it is common that LiDAR measurements are used to both correct the robot's ego-motion and to build a grid-map. In this thesis, however, the LiDAR is used solely for creating a grid-map while the ego-motion is corrected using UWB range-measurements. To quantitatively assess the mapping performance using this strategy, a HRL-SLAM algorithm was designed and implemented as a real-time application. The application is benchmarked on both synthetic- and real data and compared to similar range-based SLAM implementations. The result of this thesis indicates that UWB radios can dramatically improve the robustness of indoor localization and grid-mapping with affordable LiDARs. Secondary results are a scalability enhancement for an existing beacon-localization algorithm, a graph-topology inspired submapping-strategy and the introduction of a reference-frame independent benchmark-metric for range-SLAM. Finally, a few recommendations are done for improving the HRL-SLAM algorithm. The thesis concludes with an outlook on how to proceed from SLAM's graph-representation to a hierarchical metric-topological set of spatial representations, this can be seen as a tertiary result.