Zizhen Gu — accepted AI-conference papers

2026

Monocular Localization With Vector HD Maps Using Geometric-Context Data Association

Xinhao Liu, Shaowu Cheng, Zizhen Gu, Siqin Chen, Yong Zhao

RA-L 2026

Due to their low cost and wide field of view, monocular cameras hold great promise for visual localization. Nevertheless, significant challenges persist in associating detected semantic features with map landmarks, arising from sensing noise, depth cues, and dynamic driving scenes. Most existing app

Cited by 0 Source ↗Scholar ↗

2024

Robust Visual Localization System With HD Map Based on Joint Probabilistic Data Association

Zizhen Gu, Shaowu Cheng, Chuan Wang, Ruihan Wang, Yong Zhao

RA-L 2024

Localization based on a high-definition (HD) map is a pivotal technology for autonomous driving. Nonetheless, establishing precise data association (DA) between detected landmarks and map landmarks presents a formidable challenge when leveraging prior information on maps. Traditional DA algorithms r

Cited by 8 Source ↗Scholar ↗