  Accurate localization is a foundational capacity, required for autonomous
vehicles to accomplish other tasks such as navigation or path planning. It is a
common practice for vehicles to use GPS to acquire location information.
However, the application of GPS can result in severe challenges when vehicles
run within the inner city where different kinds of structures may shadow the
GPS signal and lead to inaccurate location results. To address the localization
challenges of urban settings, we propose a novel feature voting technique for
visual localization. Different from the conventional front-view-based method,
our approach employs views from three directions (front, left, and right) and
thus significantly improves the robustness of location prediction. In our work,
we craft the proposed feature voting method into three state-of-the-art visual
localization networks and modify their architectures properly so that they can
be applied for vehicular operation. Extensive field test results indicate that
our approach can predict location robustly even in challenging inner-city
settings. Our research sheds light on using the visual localization approach to
help autonomous vehicles to find accurate location information in a city maze,
within a desirable time constraint.
