  Fine localization is a crucial task for autonomous vehicles. Although many
algorithms have been explored in the literature for this specific task, the
goal of getting accurate results from commodity sensors remains a challenge. As
autonomous vehicles make the transition from expensive prototypes to production
items, the need for inexpensive, yet reliable solutions is increasing rapidly.
This article considers scenarios where images are captured with inexpensive
cameras and localization takes place using pre-loaded fine maps of local roads
as side information. The techniques proposed herein extend schemes based on
normalized mutual information by leveraging the likelihood of shades rather
than exact sensor readings for localization in noisy environments. This
algorithmic enhancement, rooted in statistical signal processing, offers
substantial gains in performance. Numerical simulations are used to highlight
the benefits of the proposed techniques in representative application
scenarios. Analysis of a Ford image set is performed to validate the core
findings of this work.
