  Achieving efficient and consistent localization a prior map remains
challenging in robotics. Conventional keyframe-based approaches often suffers
from sub-optimal viewpoints due to limited field of view (FOV) and/or
constrained motion, thus degrading the localization performance. To address
this issue, we design a real-time tightly-coupled Neural Radiance Fields
(NeRF)-aided visual-inertial navigation system (VINS). In particular, by
effectively leveraging the NeRF's potential to synthesize novel views, the
proposed NeRF-VINS overcomes the limitations of traditional keyframe-based maps
(with limited views) and optimally fuses IMU, monocular images, and
synthetically rendered images within an efficient filter-based framework. This
tightly-coupled fusion enables efficient 3D motion tracking with bounded
errors. We extensively compare the proposed NeRF-VINS against the
state-of-the-art methods that use prior map information and demonstrate its
ability to perform real-time localization, at over 10 Hz, on a
resource-constrained Jetson AGX Orin embedded platform.
