学术文献库

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.