Crowdsourced Semantic Edge Mapping for Autonomous Vehicles Highly accurate maps of the road infrastructure are a crucial cornerstone for selfdriving cars to enable navigation in complex traffic scenarios. Traditional methods for creating detailed maps of road environments involve expensive survey vehicles that cannot keep up with the frequent changes in the road network. In this paper, we propose a novel method to derive detailed highdefinition maps by crowd sourcing data using commodity sensors. Our system uses multisession featurebased visual SLAM to align submaps recorded by individual vehicles on a central backend server. We reconstruct 3D boundaries of road infrastructure elements such as road markings and road boundaries from semantic object contours detected in keyframes by a neural network. The result is a concise map of semantically meaningful objects suitable both for localization and higherlevel planning tasks of automated vehicles. We evaluate our method on realworld data against a globally referenced groundtruth map demonstrating a high level of detail and metric accuracy.

Exploiting Sparse Semantic HD Maps for SelfDriving Vehicle Localization In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry and LiDAR intensitybased localizers. This is important as selfdriving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensitybased approaches.