利用视觉信标与惯性推算实现无卫星信号下的城市级精准定位
A Visual-inertial Localization Algorithm using Opportunistic Visual Beacons and Dead-Reckoning for GNSS-Denied Large-scale Applications
- 基于多尺度卷积的视觉定位网络+行人惯性推算，融合卡尔曼滤波修正误差
- 在两个公开数据集上召回率提升至少3%，参数量减少63.37%，性能接近VGG16
- 适合城市峡谷等无GPS环境，尤其适用于低成本移动设备的长期定位
随着智慧城市的发展，大规模城市环境中连续行人导航需求显著增加。尽管全球导航卫星系统（GNSS）提供低成本可靠的定位服务，但在复杂城市峡谷环境中常受限制。因此，探索城市区域中的机会信号成为关键解决方案。增强现实（AR）可使行人获取实时视觉信息。为此，我们提出一种低成本视觉-惯性定位方案，包含基于轻量级多尺度组卷积（MSGC）的视觉位置识别（VPR）神经网络、行人死区推算（PDR）算法，以及基于带粗差抑制的卡尔曼滤波的视觉/惯性融合方法。VPR作为卡尔曼滤波的条件观测，有效纠正PDR累积误差。实验表明，该算法在大范围移动中保持稳定定位。相较于基于MobileNetV3的轻量级VPR方法，本方案在两个公开数据集上召回率@1提升至少3%，参数量减少63.37%；性能与基于VGG16的方法相当。VPR-PDR算法相比原始PDR定位精度提升超40%。
原文摘要 · Abstract (English)
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.
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