Look to Locate: Vision-Based Multisensory Navigation with 3-D Digital Maps for GNSS-Challenged Environments

• URL: http://arxiv.org/abs/2506.19827v1
• Date: Tue, 24 Jun 2025 17:44:03 GMT
• Title: Look to Locate: Vision-Based Multisensory Navigation with 3-D Digital Maps for GNSS-Challenged Environments
• Authors: Ola Elmaghraby, Eslam Mounier, Paulo Ricardo Marques de Araujo, Aboelmagd Noureldin,
• Abstract summary: This paper proposes a cost-effective, vision-based multi-sensor navigation system that integrates monocular depth estimation, semantic filtering, and visual map registration.<n>In real-world indoor and outdoor driving scenarios, the proposed system achieved sub-meter accuracy of 92% indoors and more than 80% outdoors.
• Score: 6.85474615630103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Global Navigation Satellite System (GNSS)-denied environments such as indoor parking structures or dense urban canyons, achieving accurate and robust vehicle positioning remains a significant challenge. This paper proposes a cost-effective, vision-based multi-sensor navigation system that integrates monocular depth estimation, semantic filtering, and visual map registration (VMR) with 3-D digital maps. Extensive testing in real-world indoor and outdoor driving scenarios demonstrates the effectiveness of the proposed system, achieving sub-meter accuracy of 92% indoors and more than 80% outdoors, with consistent horizontal positioning and heading average root mean-square errors of approximately 0.98 m and 1.25 {\deg}, respectively. Compared to the baselines examined, the proposed solution significantly reduced drift and improved robustness under various conditions, achieving positioning accuracy improvements of approximately 88% on average. This work highlights the potential of cost-effective monocular vision systems combined with 3D maps for scalable, GNSS-independent navigation in land vehicles.

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