| 面向虚实融合的单体建筑物实时识别与定位 |
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| 引用本文： | 徐舒婷,郑先伟,谢潇,熊汉江.面向虚实融合的单体建筑物实时识别与定位[J].武汉大学学报(信息科学版),2023,48(4):542-549. |
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| 作者姓名： | 徐舒婷 郑先伟 谢潇 熊汉江 |
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| 作者单位： | 1.武汉大学测绘遥感信息工程国家重点实验室，湖北 武汉，430079 |
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| 基金项目： | 国家重点研发计划2018YFB0505500国家重点研发计划2018YFB0505501国家自然科学基金41701445国家自然科学基金41871361国家自然科学基金42071370 |
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| 摘 要： | 针对当前矢量地图导航缺乏真实环境信息，而视觉地理定位依赖海量图像标注数据的问题，提出了一种面向虚实融合的单体建筑物实时识别与定位方法。该方法以智能手机为载体，利用轻量级深度网络SSD(single shot detector)实时检测手机视频流中的建筑物对象类别，通过调用手机内置传感器获取当前定位信息与拍摄视角，并以矢量地图信息为辅助，在仅需识别出建筑物类别的情况中，准确获得单个建筑物的属性与定位信息，并与矢量地图进行叠加可视化，最终达到真实地理环境与矢量地图融合的增强导航。随机采集了550张建筑物图像，经过处理标注后作为训练标签，在计算机上训练SSD的建筑检测功能并且进行验证；将训练好的SSD网络模型迁移到移动端，结合地理围栏方法与手机传感器开发可识别建筑单体信息的增强导航系统，将系统部署在手机上进行测试。实验结果表明，该方法可充分利用矢量地图与实景图片的互补信息，在仅需少量建筑物标注样本的情况下，实现单体建筑物信息增强的手机端地图导航，有效缓解了矢量地图定位不够直观的问题。
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| 关 键 词： | 地理定位 建筑物识别 嵌入式设备 增强现实导航 |
| 收稿时间： | 2020-12-16 |
Real-Time Building Instance Recognition for Vector Map and Real Scene Fusion |
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| Institution: | 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China2.School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China |
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| Abstract: | Objectives In current navigation systems, the vector map navigation lacks real environment information while visual geolocation rely heavily on massive image annotation data, leading to unsatisfactory experience for general users. This paper proposes a method of real-time recognition and positioning of single buildings for mobile augment reality (AR) navigation. Methods The proposed method adopts a lightweight deep network SSD (single shot detector) to detect in real-time the building objects from the mobile phone video stream, and obtains the current position and shooting angle of view by using the built-in sensors of the mobile phone. Once the building category is recognized, the attributes and positioning information of the involved building instances are able to be obtained by exploiting the vector map information, which are superposed on the vector map to be visualized. Thereby, an enhanced navigation system combining real geographic environment and vector map is achieved. Results The experimental results show that our proposed method can correctly identify multiple building entities at different times and locations. The building detection is less affected by lighting conditions, and the detection accuracy can reach about 95%, which meets the requirements of real-time navigation. Conclusions Compared with the traditional geolocation method, this method can make full use of the complementary information of vector maps and realistic photos, and only requires a small number of building annotation samples. This proposed method succeeds in realizing mobile AR navigation with enhanced information of individual buildings, which effectively relieves the unintuitive visualization problem of vector map navigation. This study can potentially improve users' experience and cognitive ability of environment through building detection and information enhancement. |
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