Navigating the Underground: Assessing Vision-Based SLAM Methods in Simulated Subterranean Scenarios

Abstract

This dissertation explores the viability of vision-based localization methods in subterranean environments, employing a variety of feature extraction techniques including traditional methods and advanced deep learning approaches. A unique dataset was generated using an autonomous exploration UAV within a simulated subterranean environment. This dataset served as the testing ground for evaluating various feature extraction methods. The ORB-SLAM3 was modified to integrate these methods, adapting its feature extraction module to accommodate alternative approaches while retaining its core pose optimization and backend components. The study includes detailed experiments and analyses of different sensor configurations and feature extraction methods, providing insights into their applicability and performance in subterranean settings.