LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments

TL;DR

LiDARDustX is a new LiDAR dataset focused on perception in dusty, unstructured environments like mining areas, providing valuable data for developing and benchmarking perception algorithms under challenging dust conditions.

Contribution

The paper introduces LiDARDustX, a large-scale dataset with dust-affected scenes, and establishes benchmarks for 3D detection and segmentation in high-dust environments, addressing a gap in existing datasets.

Findings

Dust significantly impacts perception accuracy.

Benchmark results reveal challenges of current algorithms in dusty conditions.

Analysis identifies causes of perception degradation due to dust.

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.