Q-Loc: Visual Cue-Based Ground Vehicle Localization Using Long Short-Term Memory

Mobile autonomous systems are increasingly being deployed in controlled environments worldwide, with large fleets of ground robots performing tasks such as delivery and surveillance. These systems require reliable localization to navigate through such environments. While the Global Positioning System (GPS) is commonly implemented in these systems, urban environments can introduce inaccuracies due to signal blockages caused by large buildings and structures, or even complete signal loss. This paper proposes a rapid and cost-effective localization method using a sensor ubiquitous in autonomous systems: cameras. We introduce a system that uses vision-based machine learning techniques to detect common landmarks in camera streams and subsequently predict location. The system employs advanced object detection models for landmark identification and recurrent neural networks for vehicle localization based on the detected landmarks. We prototype these techniques on a small-scale autonomous vehicle platform to demonstrate the system's capabilities and evaluate its accuracy and execution efficiency in real-world scenarios.