构建首个高尘非结构化道路激光雷达数据集，助力自动驾驶感知研究
LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments
- 采集6种激光雷达在3万帧高尘场景下的点云数据
- 80%以上数据含明显粉尘干扰，标注包含3D框与语义分割
- 提供基准评测平台，适合研究恶劣环境感知的开发者
自动驾驶数据集对智能车辆算法的定位、感知和预测能力验证至关重要。然而，现有数据集多聚焦于结构化城市环境，难以支持非结构化特殊场景（如矿场）中高粉尘条件下的研究。本文提出LiDARDustX数据集，专为高尘环境下感知任务设计，包含30,000帧由六种不同激光雷达采集的点云数据，每帧均配有3D边界框标注和点云语义分割标签。其中超过80%的样本受粉尘影响显著。基于该数据集，我们建立了先进3D检测与分割算法的评估基准，并分析了粉尘对感知精度的影响及其成因。数据及详情可访问：https://github.com/vincentweikey/LiDARDustX。
原文摘要 · Abstract (English)
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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