  Accurate and robust localization remains a significant challenge for
autonomous vehicles. The cost of sensors and limitations in local computational
efficiency make it difficult to scale to large commercial applications.
Traditional vision-based approaches focus on texture features that are
susceptible to changes in lighting, season, perspective, and appearance.
Additionally, the large storage size of maps with descriptors and complex
optimization processes hinder system performance. To balance efficiency and
accuracy, we propose a novel lightweight visual semantic localization algorithm
that employs stable semantic features instead of low-level texture features.
First, semantic maps are constructed offline by detecting semantic objects,
such as ground markers, lane lines, and poles, using cameras or LiDAR sensors.
Then, online visual localization is performed through data association of
semantic features and map objects. We evaluated our proposed localization
framework in the publicly available KAIST Urban dataset and in scenarios
recorded by ourselves. The experimental results demonstrate that our method is
a reliable and practical localization solution in various autonomous driving
localization tasks.
