  As the autonomous driving industry is slowly maturing, visual map
localization is quickly becoming the standard approach to localize cars as
accurately as possible. Owing to the rich data returned by visual sensors such
as cameras or LiDARs, researchers are able to build different types of maps
with various levels of details, and use them to achieve high levels of vehicle
localization accuracy and stability in urban environments. Contrary to the
popular SLAM approaches, visual map localization relies on pre-built maps, and
is focused solely on improving the localization accuracy by avoiding error
accumulation or drift. We define visual map localization as a two-stage
process. At the stage of place recognition, the initial position of the vehicle
in the map is determined by comparing the visual sensor output with a set of
geo-tagged map regions of interest. Subsequently, at the stage of map metric
localization, the vehicle is tracked while it moves across the map by
continuously aligning the visual sensors' output with the current area of the
map that is being traversed. In this paper, we survey, discuss and compare the
latest methods for LiDAR based, camera based and cross-modal visual map
localization for both stages, in an effort to highlight the strength and
weakness of each approach.
