Using Event-Based Imaging and Deep Learning to Generate 3D Surface Maps for Autonomous Roof Bolting - SME Annual Meeting 2024

This study explores implementing a machine learningbased system to generate a 3D surface repre- sentation of the roof and support straps in the mine. Event cameras have been chosen for their performance in high-dynamic-range lighting conditions and for their low latency. To enable automated drilling and bolting, 3D vision using eventbased cameras has been developed. A ground-truth set is created using two, time-synced event cameras and a LiDAR camera. These sensors are used to construct a ground-truth dataset of corresponding event- camera images and surface maps from the LiDAR. The network is tested with stereopairs of event images and produces a depth image with ±5 mm RMS error on average across 1000 test images.