Since 2020, Neural Radiance Fields, or NeRFs, have been the focus of intense interest in the scientific community for their ability to implicitly reconstruct 3D and synthesize new points of view of a scene from a limited set of images. Recent scientific advances have drastically improved initial performance (reduction in data requirements, memory needs and processing speed), paving the way for new uses of these networks, particularly in embedded applications, or for new purposes.
This thesis therefore focuses on the use of these networks for autonomous robotic navigation, with the embedded constraints involved: power consumption, limited computing and memorization hardware resources, etc. The navigation context will involve extending work already underway on incremental versions of these neural networks.
The student will be in charge of proposing and designing innovative algorithmic, software and hardware mechanisms enabling the execution of NeRFs in real time for autonomous robotic navigation.