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.

翻译：随着智慧城市的发展，大规模城市环境中连续行人导航的需求显著增加。全球导航卫星系统（GNSS）虽能提供低成本且可靠的定位服务，但在复杂的城市峡谷环境中常受阻碍。因此，探索城市区域中基于机遇性信号的定位已成为关键解决方案。增强现实（AR）技术使行人能够获取实时视觉信息。据此，我们提出一种低成本视觉-惯性定位方案。该方法包含一个基于轻量化多尺度组卷积（MSGC）的视觉位置识别（VPR）神经网络、一个行人航位推算（PDR）算法，以及一种基于带粗差抑制的卡尔曼滤波器的视觉/惯性融合方法。VPR作为卡尔曼滤波器的条件观测值，有效修正了通过PDR方法累积的误差，从而使整个算法能够确保在GNSS拒止区域中长期定位的可靠性。大量实验结果表明，我们的方法在大范围移动过程中能保持稳定的定位性能。与基于轻量化MobileNetV3的VPR方法相比，我们提出的VPR方案在两个公开数据集上将Recall@1指标提升了至少3%，同时参数量减少了63.37%，其性能亦达到与基于VGG16的方法相当的水平。相较于原始PDR，VPR-PDR算法将定位精度提升了40%以上。