CMLocate: A cross‐modal automatic visual geo‐localization framework for a natural environment without GNSS information

Autor: Zhuoqun Liu, Fan Guo, Heng Liu, Xiaoyue Xiao, Jin Tang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: IET Image Processing, Vol 17, Iss 12, Pp 3524-3540 (2023)
Druh dokumentu: article
ISSN: 1751-9667
1751-9659
DOI: 10.1049/ipr2.12883
Popis: Abstract In this paper, a new approach to visual geo‐localization for natural environments is proposed. The digital elevation model (DEM) data in virtual space is rendered and construct a panoramic skyline database is constructed. By combining the skyline database with real‐world image data (used as the “queries” to be localized), visual geo‐localization is treated as a cross‐modal image retrieval problem for panoramic skyline images, creating a unique new visual geo‐localization benchmark for the natural environment. Specifically, the semantic segmentation model named LineNet is proposed, for skyline extractions from query images, which has proven to be robust to a variety of complex natural environments. On the aforementioned benchmarks, the fully automatic method is elaborated for large‐scale cross‐modal localization using panoramic skyline images. Finally, the compound index is delicately designed to reduce the storage space of the positioning global descriptors and improve the retrieval efficiency. Moreover, the proposed method is proven to outperform most state‐of‐the‐art methods.
Databáze: Directory of Open Access Journals
Externí odkaz: