Navigating an individual in both outdoor and indoor environments would provide great facilities for the community. Image-based approaches are more affordable and feasible in comparison to the high cost of sensor devices and unavailability of guiding by GPS in indoor systems. Thus, we develop our system using cameras owing to their easy availability and accessibility. Primarily, detecting the location of personnel is needed to orientate a human. However, determining camera localization is a challenging task on the computer vision field. Therefore, in this study, a novel approach is proposed to address the problem using panoramic image sequences. We apply the scale invariant future transform for detecting corresponding points, and based on these points, a reference object is considered. The Levenberg–Marquardt optimization method is utilized to minimize reprojection errors for a given pair of images. As a better alternative to traditional methods, which apply the known surface of a reference object, our formulation rests on the scale ambiguity in different coordinate systems. Experiments are performed on Herz-Jesus-P8, Fountain-P11, and Fountain-P25 datasets to evaluate our dataset and compare our proposal with respect to prior works. We believe our proposal comparatively outperforms previous works indicating lower error rate in wide baselines between camera pairs.

在室外和室内环境中导航个人将为社区提供便利的设施。与传感器设备的高成本以及室内系统中GPS导航的可用性相比，基于图像的方法更加经济实惠且可行。因此，由于相机的易用性和可访问性，我们使用相机来开发我们的系统。首先，需要检测人员的位置以使人员定向。但是，确定相机的定位在计算机视觉领域是一项艰巨的任务。因此，在这项研究中，提出了一种新颖的方法来使用全景图像序列解决该问题。我们将尺度不变的未来变换用于检测对应点，并基于这些点，考虑参考对象。Levenberg-Marquardt优化方法用于最小化给定图像对的重投影误差。作为应用参考对象已知表面的传统方法的更好替代方法，我们的公式基于不同坐标系中的比例尺模糊性。在Herz-Jesus-P8，Fountain-P11和Fountain-P25数据集上进行了实验，以评估我们的数据集并将我们的建议与先前的工作进行比较。我们认为我们的建议相对于以前的工作表现要好，这表明摄像机对之间的宽基线中的错误率较低。在Herz-Jesus-P8，Fountain-P11和Fountain-P25数据集上进行了实验，以评估我们的数据集并将我们的建议与先前的工作进行比较。我们认为我们的建议相对于以前的工作表现要好，这表明摄像机对之间的宽基线中的错误率较低。在Herz-Jesus-P8，Fountain-P11和Fountain-P25数据集上进行了实验，以评估我们的数据集并将我们的建议与先前的工作进行比较。我们认为我们的建议相对于以前的工作表现要好，这表明摄像机对之间的宽基线中的错误率较低。