Mav Localization in GPS-Denied Environments and Synthetic Data Collection in Challenging Simulated Conditions
Abstract
The development of unmanned aerial systems presents an opportunity for conducting industrial inspections in environments where traditional navigation systems, such as the Global Navigation Satellite System (GNSS), are compromised. This dissertation investigates the implementation of a micro aerial vehicle (MAV) capable of autonomous data acquisition in complex, GNSS-degraded industrial settings. The primary challenge addressed is the robust localization of MAVs, a critical aspect in ensuring reliable operation under varying and uncertain environmental conditions. The work is divided into two main parts. The first part focuses on the design and integration of a MAV system specifically for power plant inspections in GPS-denied environments. This system leverages vision-based sensors to achieve reliable and accurate pose tracking, overcoming the limitations encountered in GPS-degraded environments of real-world industrial settings. The physical system enabled the acquisition of inspection data from hard-to-reach locations, mitigating the risks associated with manual inspections and enhancing safety. The second part of this dissertation develops a realistic simulation framework that allows for the collection of large synthetic datasets. These datasets are required for training a deep neural network (DNN) for environment classification and sensor failure detection, addressing the challenges posed by unreliable perception data in unstructured environments. The resulting data was collected in varied locations under different challenging simulated conditions, such as low light, dusty environments, or settings with lighting that creates sudden contrast changes. Key contributions of this work include the successful deployment of an autonomous MAV for power plant inspections in GPS-denied environments and the creation of a significant dataset with the potential to support the development of AI-driven localization and inspection techniques. This dataset, comprised of simulated perceptual data, is packed in a format that simplifies distribution in order to further research and development in the field. By focusing on the robustness of MAV localization for safe and efficient application in challenging environments, this research confirms the viability of MAVs as tools for autonomous data collection and validates the effectiveness of realistic simulations in creating synthetic data that can be used to improve machine learning models for autonomous systems. The outcomes of this study are expected to significantly advance the capabilities of autonomous drones in industrial applications, particularly in settings where GPS is unreliable or unavailable.
Subject Area
Robotics|Computer Engineering|Artificial intelligence
Recommended Citation
Reyes Munoz, Julio Alberto, "Mav Localization in GPS-Denied Environments and Synthetic Data Collection in Challenging Simulated Conditions" (2024). ETD Collection for University of Texas, El Paso. AAI31297780.
https://scholarworks.utep.edu/dissertations/AAI31297780