A Visual-inertial Localization Algorithm using Opportunistic Visual Beacons and Dead-Reckoning for GNSS-Denied Large-scale Applications

TL;DR

This paper introduces a low-cost visual-inertial localization method for GNSS-denied urban environments, combining a neural network-based visual place recognition, dead reckoning, and Kalman filter fusion to achieve stable, accurate positioning.

Contribution

It presents a novel lightweight VPR neural network and an integrated fusion approach that significantly improves long-term localization accuracy in large-scale urban areas.

Findings

Improves Recall@1 by at least 3% over MobileNetV3-based VPR

Reduces VPR model parameters by 63.37%

Enhances localization accuracy by over 40% compared to original PDR

Abstract

With the development of smart cities, the demand for continuous pedestrian navigation in large-scale urban environments has significantly increased. While global navigation satellite systems (GNSS) provide low-cost and reliable positioning services, they are often hindered in complex urban canyon environments. Thus, exploring opportunistic signals for positioning in urban areas has become a key solution. Augmented reality (AR) allows pedestrians to acquire real-time visual information. Accordingly, we propose a low-cost visual-inertial positioning solution. This method comprises a lightweight multi-scale group convolution (MSGC)-based visual place recognition (VPR) neural network, a pedestrian dead reckoning (PDR) algorithm, and a visual/inertial fusion approach based on a Kalman filter with gross error suppression. The VPR serves as a conditional observation to the Kalman filter, effectively correcting the errors accumulated through the PDR method. This enables the entire algorithm to ensure the reliability of long-term positioning in GNSS-denied areas. Extensive experimental results demonstrate that our method maintains stable positioning during large-scale movements. Compared to the lightweight MobileNetV3-based VPR method, our proposed VPR solution improves Recall@1 by at least 3\% on two public datasets while reducing the number of parameters by 63.37\%. It also achieves performance that is comparable to the VGG16-based method. The VPR-PDR algorithm improves localization accuracy by more than 40\% compared to the original PDR.