Hierarchical Training Pipeline for Event-Based Robotic Perception Models for Autonomous Roof Bolting - SME Annual Meeting 2024

Event cameras are used for their performance in high dynamic-range lighting conditions which are canonical to active mining environments. Direct labeling of event-based image data to train a model to perform semantic segmentation using traditional methods is slow and error-prone. This study proposes a framework to use roughly hand-labeled color images from a mine as an input to an intermediary probabilistic algorithm called alphamatting to generate a ground-truth data set. These high-fidelity labels can be used to train a semantic segmentation model to differentiate the support strap from the roof. This model can then be leveraged to segment an event-based scene to enable autonomous roof bolting. This pipeline has been shown to achieve an accuracy of 88% with a false positive rate of 3%.