Monocular Localization with Semantics Map for Autonomous Vehicles

TL;DR

This paper introduces a lightweight semantic localization method for autonomous vehicles that uses stable semantic features for more robust and efficient localization compared to traditional texture-based approaches.

Contribution

A novel semantic-based localization algorithm that constructs offline semantic maps and performs online data association, improving robustness and efficiency in autonomous vehicle localization.

Findings

Demonstrated robustness in diverse urban scenarios

Achieved reliable localization with reduced computational cost

Validated on KAIST Urban dataset and custom recordings

Abstract

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.