Monocular Localization with Semantics Map for Autonomous Vehicles
This addresses localization for autonomous vehicles, offering a more efficient and robust solution compared to traditional methods, though it appears incremental as it builds on existing semantic mapping approaches.
The paper tackles the challenge of accurate and robust localization for autonomous vehicles by proposing a lightweight visual semantic localization algorithm that uses stable semantic features instead of low-level texture features, demonstrating reliability and practicality in various autonomous driving tasks on the KAIST Urban dataset and custom scenarios.
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.