Vision-based mapping of lane semantics and topology for intelligent vehicles

Abstract

High-definition map is an essential tool for route measurement, planning and navigation of intelligent vehicles. Yet its creation is still a persisting challenge, especially in creating the semantic and topology layer of the map based on visual sensing. However, current semantic mapping approaches do not consider the map applicability in navigation tasks while the topology mapping approaches face the issues of limited location accuracy or expensive hardware cost. In this paper, we propose a joint mapping framework for both semantic and topology layers, which are learned in a lane-level and based on a monocular camera sensor and an on-board GPS positioning device. A map management approach “RoadSegDict” is also proposed to support the efficient updating of semantic map in a crowdsourced manner. Moreover, a new dataset is proposed, which includes a variety of lane structures with detailed semantic and topology annotations.