In this article, we report about the first results of the mapKITE system, a tandem terrestrial-aerial concept for geodata acquisition and processing, obtained in corridor mapping missions. The system combines an Unmanned Aerial System (UAS) and a Terrestrial Mobile Mapping System (TMMS) operated in a singular way: real-time waypoints are computed from the TMMS platform and sent to the UAS in a follow-me scheme. This approach leads to a simultaneous acquisition of aerial-plus-ground geodata and, moreover, opens the door to an advanced post-processing approach for sensor orientation. The current contribution focuses on analysing the impact of the new, dynamic Kinematic Ground Control Points (KGCPs), which arise inherently from the mapKITE paradigm, as an alternative to conventional, costly Ground Control Points (GCPs). In the frame of a mapKITE campaign carried out in June 2016, we present results entailing sensor orientation and calibration accuracy assessment through ground check points, and precision and correlation analysis of self-calibration parameters’ estimation. Conclusions indicate that the mapKITE concept eliminates the need for GCPs when using only KGCPs plus a couple of GCPs at each corridor end, achieving check point horizontal accuracy of ${\mu }_{E,N}\approx 1.7$ px (3.4 cm) and ${\mu }_h\approx 4.3$ px (8.6 cm). Since obtained from a simplified version of the system, these preliminary results are encouraging from a future perspective.

Autonomous flight with unmanned aerial vehicles (UAVs) nowadays depends on the availability and reliability of Global Navigation Satellites Systems (GNSS). In cluttered outdoor scenarios, such as narrow gorges, or near tall artificial structures, such as bridges or dams, reduced sky visibility and multipath effects compromise the quality and the trustworthiness of the GNSS position fixes, making autonomous, or even manual, flight difficult and dangerous. To overcome this problem, cooperative navigation has been proposed: a second UAV flies away from any occluding objects and in line of sight from the first and provides the latter with positioning information, removing the need for full and reliable GNSS coverage in the area of interest. In this work we use high-power light-emitting diodes (LEDs) to signalize the second drone and we present a computer vision pipeline that allows to track the second drone in real-time from a distance up to 100 m and to compute its relative position with decimeter accuracy. This is based on an extension to the classical iterative algorithm for the Perspective-n-Points problem in which the photometric error is minimized according to a image formation model. This extension allow to substantially increase the accuracy of point-feature measurements in image space (up to 0.05 pixels), which directly translates into higher positioning accuracy with respect to conventional methods.