Use of Assisted Photogrammetry for Indoor and Outdoor Navigation Purposes

Abstract

ABSTRACT: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 imagebased 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.

An aerial/ground robot team for autonomous firefighting in urban GNSS-denied scenarios

“…However RGBD and time-of-flight cameras cannot work under strong sunlight, and stereo cameras would need a large baseline in order to accurately estimate distances at 25 meters approximately (as the case of Challenge 3). Bundle adjustment approaches (Cucci et al, 2017;Pagliari et al, 2015) can be used offline in combination to visual cameras or short-baseline stereo to build accurate maps outdoors, but they cannot provide reliable odometry during the robot operation. On the other hand, 3D-LIDAR approaches work outdoors, range up to 120 meters and can perceive the 360º of the robot's environment.…”

Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

“…[11][12][13][14] However, these approaches tend to fail when the aerial vehicle exhibits high-speed motions. Other approaches make use of photogrammetry 15 but pose additional processing requirements that hinder online computation.…”

A REVIEW OF RECENT RESEARCH IN INDOOR MODELLING & MAPPING

“…Sensors that acquire indoor information can also be used in aiding navigation for instance RGB-D cameras (Kanai et al, 2015) and commercial ones such as Microsoft Kinect were used for aiding indoor navigation. (Pagliari et al, 2015). It is also possible to generate point clouds from video recordings.…”