Papers › Opendenselane: A Dense Lidar-Based Dataset for HD Map Construction
Opendenselane: A Dense Lidar-Based Dataset for HD Map Construction
Xiaolei Chen, Wenlong Liao, Bin Liu, Junchi Yan, Tao He
In autonomous driving system, High-Definition (HD) map is an important basis for localization, perception and planning tasks. For the construction of HD map, land marking detection is the first step. Recent studies of land marking detection are mainly based on camera image data, while LiDAR-based land marking detection is rarely studied. The main reason is that there are few datasets specially developed for land marking detection. In this paper, a new LiDAR-based land marking dataset named OpenDenseLane is developed for the construction of HD map, and is released to support the academic research. OpenDenseLane contains 1,709 scenarios with 57,227 frames, and each frame includes two types of LiDAR point cloud, camera image and localization data. The LiDAR data in our dataset are dense point clouds to reduce the impact of sparse point distribution. OpenDense-Lane provides abundant annotations of ground signs, such as lane line, crosswalk and turn arrow. Experiments are conducted on the proposed dataset and the results of land marking detection and HD map construction are analysed. Open-DenseLane will be released at https://github.com/Thinklab-SJTU/OpenDenseLane.
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