Dust De-Filtering in LiDAR Applications With Conventional and CNN Filtering Methods

Abstract

Light detection and ranging (LiDAR) sensors can create high-quality scans of an environment. However, LiDAR point clouds are affected by harsh weather conditions since airborne particles are easily detected. In literature, conventional filtering and artificial intelligence (AI) filtering methods have been used to detect, and remove, airborne particles. In this paper, a convolutional neural network (CNN) model was used to classify airborne dust particles through a voxel-based approach. The CNN model was compared to several conventional filtering methods, where the results show that the CNN filter can achieve up to 5.39% F1 score improvement when compared to the best conventional filter. All the filtering methods were tested in dynamic environments where the sensor was attached to a mobile platform, the environment had several moving obstacles, and there were multiple dust cloud sources.

Keywords

• Artificial intelligence (AI)
• autonomous navigation
• convolutional neural network (CNN)
• dust de-filtering
• light detection and ranging (LiDAR)