Computationally efficient dust mitigation for LiDAR sensors in lunar and terrestrial environments

LiDAR sensors are essential for applications such as autonomous vehicles and space exploration but experience performance degradation in dusty environments. Airborne particles introduce noise into point cloud data, leading to false obstacle detections. In lunar missions, this can trigger unnecessary braking, wasting time and resources. Machine Learning (ML) methods have been investigated for dust filtering but require large, labeled datasets and demand high computational resources—limitations that make them unsuitable for resource- constrained platforms like space rovers. This paper presents a dust removal technique that avoids labeled data requirements while improving computational efficiency. Like the Low Intensity Dynamic Radius Outlier Removal (LIDROR) method, it exploits the low intensity returns of dusty particles to identify and remove outliers based on point density. However, unlike LIDROR’s computational expensive 3D density search, the proposed method applies spatial segmentation and Principal Component Analysis (PCA) to reduce the data from 3D to 2D before filtering, significantly accelerating processing. Unlike PCA Adaptive Clustering (PCAAC), which uses PCA for dimensionality reduction but relies on the computationally heavy Density-Based Spatial Clustering of Application and Noise (DBSCAN), the proposed method avoids DBSCAN inefficiencies and poor scalability for large datasets. By combining intensity-based candidate selection, segmentation, PCA-driven 2D projection, and optimized outlier detection, the method delivers an efficient and scalable solution. Experimental results show that the proposed approach removes dust noise effectively, achieving high precision, recall, and F1 scores while maintaining low processing times. These characteristics make it well-suited for planetary exploration and autonomous navigation in challenging dusty environments.

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