5 accepted papers
A major bottleneck in off-road autonomous driving research lies in the scarcity of large-scale, high-quality datasets and benchmarks. To bridge this gap, we present ORAD-3D, which, to the best of our knowledge, is the largest dataset specifically curated for off-road autonomous driving. ORAD-3D cove…
In the field of autonomous driving, constructing high-precision maps, typically represented as 3D point cloud maps or bird's-eye view (BEV) grid maps, is essential for both offline and online applications. However, the presence of dynamic objects within a scene can introduce artifacts and noise that…
In the field of autonomous driving, constructing high-precision maps, typically represented as 3D point cloud maps or bird's-eye view (BEV) grid maps, is essential for both offline and online applications. However, the presence of dynamic objects within a scene can introduce artifacts and noise that
In this paper, we propose a multi-environment, multi-LiDAR, multi-task dataset to promote the grid map-based navigation capability for autonomous vehicles. The dataset comprises structured and unstructured environmental data captured by different types of LiDAR and contains various challenging scena
For autonomous driving, drivable region detection is one of the most basic and essential tasks. In this paper, a novel LiDAR-based drivable region detection algorithm which could output a complete, accurate and stable result is proposed. To promote the completeness of the detection result, the Bayes…