自動運転車のためのセマンティックマップを用いた単眼自己位置推定
Monocular Localization with Semantics Map for Autonomous Vehicles
低レベルのテクスチャ特徴の代わりに安定したセマンティック特徴を用いて軽量な視覚的自己位置推定を行い、KAIST Urbanデータセットなどで有効性を実証した論文。
著者: Jixiang Wan, Xudong Zhang, Shuzhou Dong, Yuwei Zhang, Yuchen Yang, Ruoxi Wu, Ye Jiang, Jijunnan Li, Jinquan Lin, Ming Yang
分類: cs.CV, cs.RO
原文アブストラクト
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.
