Dust Filtering in LIDAR Point Clouds Using Deep Learning for Mining Applications

In the domain of mining and mineral processing, depth sensors, including LIDAR and 3D cameras, are employed to obtain precise three-dimensional measurements of the surrounding environment. However, the functionality of these sensors is hindered by the dust produced by mining operations. In order to address this problem, a neural network-based method is proposed. This method is capable of filtering dust measurements in real time from point clouds obtained using LIDARs. The proposed method is trained and validated using both real and simulated data, yielding results that are at the forefront of the field. Furthermore, a public database is constructed using LIDAR sensor data from diverse simulated and real dusty environments. The database will be made public for use in the training and benchmarking of dust filtering methods.