Vision-Based Localization in Dense Urban Environments: A Case Study of an Urban Village in China
Quick Take
This study presents a vision-based geo-localization solution for dense urban environments, specifically targeting the challenges faced in urban villages like Shipai in Guangzhou. Utilizing a dual-camera system for data collection, the approach aims to improve navigation and emergency response in areas with poor GPS coverage, ultimately benefiting vulnerable migrant populations.
Key Points
- Urban villages face unreliable GPS signals due to dense building arrangements.
- A dual-camera system captures synchronized 360-degree panoramas for geo-localization.
- The study develops a specialized image geo-localization dataset for performance assessment.
- Findings highlight both strengths and limitations of visual-based localization methods.
- The framework supports pedestrian navigation and emergency management in informal settlements.
Article Content
From source RSS / original summaryarXiv:2605. 30714v1 Announce Type: new Abstract: Urban villages, the widespread informal settlements which have emerged as a result of rapid urbanization, are now major residential hubs for migrant workers in large cities in China. The dense arrangement of buildings in these areas often leads to unreliable GPS signals, while incomplete mapping data further impairs accurate route planning and navigation.
These issues not only hinder everyday mobility but also pose significant challenges for emergency response, as confusing road layouts and GPS inaccuracies can complicate evacuation efforts. To address these challenges, we propose a practical vision-based geo-localization solution tailored for dense urban environments.
Our approach features a low-cost data collection pipeline utilizing a dual-camera system, comprising a panoramic camera and a smartphone camera, to capture synchronized 360-degree panoramas and query images. Using Shipai Village, a well-known densely populated urban village in Guangzhou, as a case study, we develop a specialized image geo-localization dataset. We then assess and compare the performance of existing models across various scene types to identify their strengths and weaknesses.
The findings demonstrate both the potential and limitations of visual-based localization in dense urban-village environments. Our framework aims to enhance pedestrian navigation, last-mile delivery, and emergency management in areas with poor GPS coverage, ultimately supporting the vulnerable populations living within these informal settlements.
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