Abstract
GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.
Highlights
One of the most important factors for Global Navigation Satellite Systems (GNSS) in terms of desired positioning accuracy is ample satellite coverage
We propose a methodology that enables the localization of arbitrary points in an area of interest using only a single camera mounted on a commercial or other unmanned aerial vehicle (UAV or drone)
The presented methodology is based on the OrbSlam2 [25] method, due to its performance compared to other monocular slam methods [26,27] which allows a Unmanned Aerial Vehicle (UAV) equipped with a camera to map its surroundings and localize itself in an unknown environment
Summary
One of the most important factors for Global Navigation Satellite Systems (GNSS) in terms of desired positioning accuracy is ample satellite coverage. GNSS sufficiency can be degraded in areas where the surrounding land morphology and geometry occludes or interacts with the signal between the satellites and the receiver. Urban environments and vegetated areas pose challenges to precise GNSS positioning because of signal interference, multipath effect or line of sight occlusion, factors which do not necessarily decrease over time during the measurement [1]. Even a satellite signal blockage of short duration can significantly degrade performance in navigation systems. There are cases where typical surveying cannot be substituted at the moment from GNSS, while in other cases classic surveying remains impractical. There is an unresolved set of circumstances, where the need of cost-effective rapid mapping in GPS-denied environments remains crucial
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