An Attention-Based Deep Learning Architecture for Real-Time Monocular Visual Odometry: Applications to GPS-free Drone Navigation

arXiv:2404.17745v1 Announce Type: cross
Abstract: Drones are increasingly used in fields like industry, medicine, research, disaster relief, defense, and security. Technical challenges, such as navigation in GPS-denied environments, hinder further adoption. Research in visual odometry is advancing, potentially solving GPS-free navigation issues. Traditional visual odometry methods use geometry-based pipelines which, while popular, often suffer from error accumulation and high computational demands. Recent studies utilizing deep neural networks (DNNs) have shown improved performance, addressing these drawbacks. Deep visual odometry typically employs …

abstract adoption applications architecture arxiv attention challenges cs.cv cs.lg cs.ro deep learning defense disaster drone drones eess.iv environments fields free gps hinder industry medicine navigation real-time research security technical type visual