We present CamNav, a vision-based navigation system that provides users with indoor navigation services. CamNav captures images in real time while the user is walking to recognize their current location. It does not require any installation of indoor localization devices. In this paper, we describe the techniques of our system that improve the recognition accuracy of an existing system that uses oriented FAST and rotated BRIEF (ORB) as part of its location-matching procedure. We employ multiscale local binary pattern (MSLBP) features to recognize places. We implement CamNav and conduct required experiments to compare the obtained accuracy when using ORB, the scale-invariant feature transform (SIFT), MSLBP features, and the combination of both ORB and SIFT features with MSLBP. A dataset composed of 42 classes was constructed for assessment. Each class contains 100 pictures designed for training one location and 24 pictures dedicated for testing. The evaluation results demonstrate that the place recognition accuracy while using MSLBP features is better than the accuracy when using SIFT features. The accuracy when using SIFT, MSLBP, and ORB features is 88.19%, 91.27%, and 96.33%, respectively. The overall accuracy of recognizing places increased to 93.55% and 97.52% after integrating MSLBP with SIFT with ORB, respectively.

我们介绍了CamNav，这是一种基于视觉的导航系统，可为用户提供室内导航服务。当用户行走以识别其当前位置时，CamNav实时捕获图像。它不需要任何室内定位设备的安装。在本文中，我们描述了我们的系统技术，该技术可提高使用定向FAST和旋转的Brief（ORB）作为其位置匹配过程一部分的现有系统的识别精度。我们采用多尺度局部二进制模式（MSLBP）功能来识别位置。我们实施CamNav并进行必要的实验，以比较使用ORB，尺度不变特征变换（SIFT），MSLBP特征以及ORB和SIFT特征与MSLBP的组合时获得的精度。构建了由42个类别组成的数据集以进行评估。每节课包含用于训练一个位置的100张图片和用于测试的24张图片。评估结果表明，使用MSLBP功能时的位置识别准确度优于使用SIFT功能时的位置识别准确度。使用SIFT，MSLBP和ORB功能时的准确性分别为88.19％，91.27％和96.33％。将MSLBP与SIFT和ORB集成后，识别位置的总体准确性分别提高到93.55％和97.52％。