Indoor pedestrian localization measurement is a hot topic and is widely used in indoor navigation and unmanned devices. PDR (Pedestrian Dead Reckoning) is a low-cost and independent indoor localization method, estimating position of pedestrians independently and continuously. PDR fuses the accelerometer, gyroscope and magnetometer to calculate relative distance from starting point, which is mainly composed of three modules: step detection, stride length estimation and heading calculation. However, PDR is affected by cumulative error and can only work in two-dimensional planes, which makes it limited in practical applications. In this paper, a novel localization method V-PDR is presented, which combines VPR (Visual Place Recognition) and PDR in a loosely coupled way. When there is error between the localization result of PDR and VPR, the algorithm will correct the localization of PDR, which significantly reduces the cumulative error. In addition, VPR recognizes scenes on different floors to correct floor localization due to vertical movement, which extends application scene of PDR from two-dimensional planes to three-dimensional spaces. Extensive experiments were conducted in our laboratory building to verify the performance of the proposed method. The results demonstrate that the proposed method outperforms general PDR method in accuracy and can work in three-dimensional space.

中文翻译：

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基于图像检索和航位推算的室内定位新方法

室内行人定位测量是一个热门话题，广泛用于室内导航和无人驾驶设备。行人航位推算（PDR）是一种低成本且独立的室内定位方法，可独立，连续地估算行人的位置。PDR融合了加速度计，陀螺仪和磁力计来计算距起点的相对距离，该距离主要由三个模块组成：步距检测，步幅估计和航向计算。但是，PDR受累积误差的影响，并且只能在二维平面上工作，这使其在实际应用中受到限制。本文提出了一种新颖的定位方法V-PDR，它以松散耦合的方式结合了VPR（视觉位置识别）和PDR。当PDR和VPR的定位结果之间存在误差时，该算法将纠正PDR的本地化，从而大大减少了累积误差。此外，VPR可以识别不同楼层的场景，以纠正由于垂直移动而导致的楼层定位，从而将PDR的应用场景从二维平面扩展到三维空间。在我们的实验室中进行了广泛的实验，以验证所提出方法的性能。结果表明，该方法在精度上优于一般的PDR方法，可以在三维空间中工作。在我们的实验室中进行了广泛的实验，以验证所提出方法的性能。结果表明，该方法在精度上优于一般的PDR方法，可以在三维空间中工作。在我们的实验室中进行了广泛的实验，以验证所提出方法的性能。结果表明，该方法在精度上优于一般的PDR方法，可以在三维空间中工作。