LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments

• URL: http://arxiv.org/abs/2505.21914v1
• Date: Wed, 28 May 2025 02:59:19 GMT
• Title: LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments
• Authors: Chenfeng Wei, Qi Wu, Si Zuo, Jiahua Xu, Boyang Zhao, Zeyu Yang, Guotao Xie, Shenhong Wang,
• Abstract summary: This paper introduces the LiDARDustX dataset, which is specifically designed for perception tasks under high-dust conditions.<n>We have established a benchmark for evaluating the performance of state-of-the-art 3D detection and segmentation algorithms.
• Score: 13.718497912747743
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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