Abstract
Abstract. High-Definition Maps (HD Maps) are lane-level 3-dimensional road network models that ensure the safe operation of autonomous vehicles and drive the continuous advancement of autonomous driving technology to higher levels. However, HD Maps currently face challenges regarding limited coverage and description of indoor environments (e.g. underground parking), as well as the lack of uniformity between indoor and outdoor map formats. Therefore, this paper proposes a Layered HD Map model for both indoor and outdoor environments. The map model consists of 6 layers, allowing effective representation of indoor and outdoor environments, as well as transitional areas. The paper also establishes an Unmanned Ground Vehicle (UGV) to collect and update map data using LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) and autonomous exploration technology, aiming to enhance the efficiency and automation of HD Map acquisition. In order to verify the validity of the proposed map model, this paper conducted a navigation experiment in a double decker parking lot containing entrances and exits. The results of the experiments demonstrate that the Layered HD Map generated through autonomous LiDAR SLAM can effectively describe the indoor and outdoor environments, and enable successful navigation of UGVs.
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