  Vehicle localization is essential for autonomous vehicle (AV) navigation and
Advanced Driver Assistance Systems (ADAS). Accurate vehicle localization is
often achieved via expensive inertial navigation systems or by employing
compute-intensive vision processing (LiDAR/camera) to augment the low-cost and
noisy inertial sensors. Here we have developed a framework for fusing the
information obtained from a smart infrastructure node (ix-node) with the
autonomous vehicles on-board localization engine to estimate the robust and
accurate pose of the ego-vehicle even with cheap inertial sensors. A smart
ix-node is typically used to augment the perception capability of an autonomous
vehicle, especially when the onboard perception sensors of AVs are blocked by
the dynamic and static objects in the environment thereby making them
ineffectual. In this work, we utilize this perception output from an ix-node to
increase the localization accuracy of the AV. The fusion of ix-node perception
output with the vehicle's low-cost inertial sensors allows us to perform
reliable vehicle localization without the need for relying on expensive
inertial navigation systems or compute-intensive vision processing onboard the
AVs. The proposed approach has been tested on real-world datasets collected
from a test track in Ann Arbor, Michigan. Detailed analysis of the experimental
results shows that incorporating ix-node data improves localization
performance.
