UC San Diego

Recent advancements in statistical learning and computational abilities have enabled autonomous vehicle technology to develop at a much faster rate. While many self-driving architectures previously introduced are capable of operating under highly dynamic environments, many of these are constrained to smaller-scale deployments, heavy dependence on high-definition (HD) maps that require constant maintenance and tedious manual labeling. This thesis is trying to address the mapping problem in the context of autonomous vehicle applications. We study the existing map representations in the literature and analyze the performance of parametric mapping algorithms in both indoor and outdoor environments. We then propose to fuse image and pre-built point cloud map information to perform automatic and accurate labeling of static landmarks such as roads, sidewalks, crosswalks, and lanes. The method performs semantic segmentation on 2D images, associates the semantic labels with point cloud maps to accurately localize them in the world, and leverages the confusion matrix formulation to construct a probabilistic semantic map in bird's eye view from semantic point clouds. Experiments from data collected in an urban environment show that this model is able to predict most road features and can be extended for automatically incorporating road features into HD maps with potential future work directions.