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

TL;DR

This paper introduces a vision-based multisensory navigation system using 3-D digital maps that achieves high accuracy in GNSS-challenged environments, demonstrating robustness and significant drift reduction in real-world tests.

Contribution

It presents a novel integration of monocular depth estimation, semantic filtering, and visual map registration with 3-D maps for scalable GNSS-independent vehicle navigation.

Findings

Achieves 92% indoor and 80% outdoor sub-meter accuracy.

Reduces drift and improves robustness compared to baselines.

Enhances positioning accuracy by approximately 88%.

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.