Highly accurate maps of the road infrastructure are a crucial cornerstone for self-driving 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 high-definition maps by crowd sourcing data using commodity sensors. Our system uses multi-session feature-based 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 higher-level planning tasks of automated vehicles. We evaluate our method on real-world data against a globally referenced ground-truth map demonstrating a high level of detail and metric accuracy.     «

Highly accurate maps of the road infrastructure are a crucial cornerstone for self-driving 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 high-definition maps by crowd sourcing data using commodity sensors. Our system uses multi-session feature-based visual SLAM...     »