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USE OF ASSISTED PHOTOGRAMMETRY FOR INDOOR AND OUTDOOR NAVIGATION PURPOSES

机译:使用辅助摄影测量室室内和室外航行的目的

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Nowadays, devices and applications that require navigation solutions are continuously growing. For instance, consider the increasing demand of mapping information or the development of applications based on users' location. In some case it could be sufficient an approximate solution (e.g. at room level), but in the large amount of cases a better solution is required. The navigation problem has been solved from a long time using Global Navigation Satellite System (GNSS). However, it can be unless in obstructed areas, such as in urban areas or inside buildings. An interesting low cost solution is photogrammetry, assisted using additional information to scale the photogrammetric problem and recovering a solution also in critical situation for image-based methods (e.g. poor textured surfaces). In this paper, the use of assisted photogrammetry has been tested for both outdoor and indoor scenarios. Outdoor navigation problem has been faced developing a positioning system with Ground Control Points extracted from urban maps as constrain and tie points automatically extracted from the images acquired during the survey. The proposed approach has been tested under different scenarios, recovering the followed trajectory with an accuracy of 0.20 m. For indoor navigation a solution has been thought to integrate the data delivered by Microsoft Kinect, by identifying interesting features on the RGB images and re-projecting them on the point clouds generated from the delivered depth maps. Then, these points have been used to estimate the rotation matrix between subsequent point clouds and, consequently, to recover the trajectory with few centimeters of error.
机译:如今,需要导航解决方案的设备和应用程序不断增长。例如,考虑基于用户位置映射信息或应用程序的开发的日益增加。在某些情况下,它可能是近似解(例如在房间)的情况下,但在大量情况下需要更好的解决方案。使用全局导航卫星系统(GNSS)已经长时间解决了导航问题。然而,除非在受阻区域,例如在城市地区或建筑物内。一个有趣的低成本解决方案是摄影测量,辅助使用其他信息来缩放摄影测量问题并在基于图像的方法的危急情况下恢复解决方案(例如纹理表面不良)。在本文中,已经测试了辅助摄影测量的使用,用于户外和室内场景。户外导航问题已经面临着从城市地图中提取的地面控制点的定位系统,因为约束和扎带从调查期间获取的图像自动提取。所提出的方法已经在不同场景下进行了测试,以0.20米的精度恢复遵循的轨迹。对于室内导航,通过识别RGB图像上的有趣功能并将其重新投影在从传递的深度映射生成的点云上重新投影它们,已经认为解决了解到Microsoft Kinect提供的数据。然后,已经使用这些点来估计随后的点云之间的旋转矩阵,因此,以几厘米的误差恢复轨迹。

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