Autonomous Navigation using Event Cameras for Off-road Environments

Abstract

Robotic automation has always been employed to optimize tasks that are deemed repetitive or hazardous for humans. One example of such an application is within transportation, be it in commercial or military settings. In such scenarios, it is required for the platform’s operator to be at a heightened level of awareness at all times to ensure the safety of on-board material. Additionally, during longer journeys it is often the case that the driver might also be required to traverse difficult terrain under harsh environments, for instance in low light, fog, or haze-ridden paths. To counter this issue, recent studies have proven that the assistance of smart systems is necessary to minimize the risk involved. In order to develop said systems, this M.Sc. project proposes a Deep Learning based Vision Navigation algorithm capable of terrain analysis and determining the appropriate steering angle within a margin of confidence. The developed algorithm shall also tackle several other issues within the development of autonomous systems, like power consumption, latency, and environmental robustness. These factors are addressed by using neuromorphic vision sensors; Event Cameras.

Date of Award	Aug 2023
Original language	American English
Supervisor	Majid Khonji (Supervisor)