Autonomous driving technology is now evolving at an unprecedented speed. HD maps, which are embedded with highly precise and detailed road spatial and object information, play an important role in supporting autonomous vehicles. This thesis presents the development of a semi-automated HD map creation and updating method that is capable of extracting basic road feature information to HD maps by employing raw MLS point cloud data. The proposed HD map creation method consists of four steps: Road edge extraction, road surface extraction, road marking extraction and driving line generation. First, an existing curb-based road edge detection method is applied to extract road edge candidate points according to the elevation difference and slope between points. This thesis develops an edge vectorization algorithm based on the point's distance-to-trajectory. Then, the road surface is extracted by filtering the points inside fitted edges on the XY plane within a range of the ground elevation. In the next step, instead of using intensity to detect road markings used by most studies, this thesis fuses point clouds and images to assign each point with an RGB value to segment marking points. Marking objects are extracted by conditional Euclidean clustering and classified according to a manually defined decision tree. Finally, driving lines are generated based on the vectorized road edge and lane markings. The HD map update method varies depending on which data source is updated for the road segments, including updating images only, updating point clouds only and updating both images and point clouds. The method is evaluated by six point clouds and image datasets collected from different types of roads. The extracted road edges are assessed by both length- and buffer-based assessment methods. The results indicate that the road edge extraction algorithm performs well in all three dimensions. The road surface extraction results confirm the high accuracy of extracted edges. In addition, the quantitative evaluations of road markings demonstrate that the proposed road marking extraction method achieves an average recall, precision, and F1-score of 94.50%, 81.65% and 87.09%.

The ability to position yourself and map the surroundings is an important aspect for both civilian and military applications. Global navigation satellite systems are very popular and are widely used for positioning. This kind of system is however quite easy to disturb and therefore lacks robustness. The introduction of autonomous vehicles has accelerated the development of local positioning systems. This thesis work is done in collaboration with FOI in Linköping, using a positioning system with LIDAR and IMU sensors in a EKF-SLAM system using the GTSAM framework. The goal was to evaluate the system in different conditions and also investigate the possibility of using the road surface for positioning. Data available at FOI was used for evaluation. These data sets have a known sensor setup and matches the intended hardware. The data sets used have been gathered on three different occasions in a residential area, a country road and a forest road in sunny spring weather on two occasions and one occasion in winter conditions. To evaluate the performance several different measures were used, common ones such as looking at positioning error and RMSE, but also the number of found landmarks, the estimated distance between landmarks and the drift of the vehicle. All results pointed towards the forest road providing the best positioning, the country road the worst and the residential area in between. When comparing different weather conditions the data set from winter conditions performed the best. The difference between the two spring data sets was quite different which indicates that there may be other factors at play than just weather. A road edge detector was implemented to improve mapping and positioning. Vectors, denoted road vectors, with position and orientation were adapted to the edge points and the change between these road vectors were used in the system using GTSAM in areas with few landmarks. The clearest improvements to the drift in the vehicle direction was in the longer country area where the error was lowered with 6.4 % with increase in the error sideways and in orientation as side effects. The implemented method has a significant impact on the computational cost of the system as well as requiring precise adjustment of uncertainty to have a noticeable improvement and not worsen the overall results.