LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments
This addresses a domain-specific problem for autonomous driving in mining and similar dusty environments, but it is incremental as it primarily provides a new dataset rather than a novel method.
The paper tackles the lack of datasets for autonomous driving in dusty unstructured environments by introducing LiDARDustX, a dataset with 30,000 LiDAR frames and annotations, and uses it to benchmark 3D detection and segmentation algorithms, analyzing dust's impact on perception accuracy.
Autonomous driving datasets are essential for validating the progress of intelligent vehicle algorithms, which include localization, perception, and prediction. However, existing datasets are predominantly focused on structured urban environments, which limits the exploration of unstructured and specialized scenarios, particularly those characterized by significant dust levels. This paper introduces the LiDARDustX dataset, which is specifically designed for perception tasks under high-dust conditions, such as those encountered in mining areas. The LiDARDustX dataset consists of 30,000 LiDAR frames captured by six different LiDAR sensors, each accompanied by 3D bounding box annotations and point cloud semantic segmentation. Notably, over 80% of the dataset comprises dust-affected scenes. By utilizing this dataset, we have established a benchmark for evaluating the performance of state-of-the-art 3D detection and segmentation algorithms. Additionally, we have analyzed the impact of dust on perception accuracy and delved into the causes of these effects. The data and further information can be accessed at: https://github.com/vincentweikey/LiDARDustX.