Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Centimetre level precision mapping is essential for many applications such as land-use, infrastructure inspection, cultural heritage preservation, and construction site monitoring. However, the acquisition and its preparation (in particular the setting of a ground control point network (GCPs)) are still expensive or even impossible in cluttered or dangerous areas. The recent development of UAVs together with the miniaturization of the sensors is a promising evolution for reducing costs and expand opportunities.
The sensors embedded on the drone: GNSS antenna, IMU, camera and (optional) LIDAR are light and often low-cost. The low quality of their raw measurements must be counterbalanced by their rigorous modeling in order to obtain accurate final results: if we cannot expect the sensors to be error-free, one must model these in order to correct them. This is achieved by in-situ calibration or on a dedicated calibration field, together with a rigorous fusion of the raw data acquired by the different sensors with the so-called bundle-adjustment method.
This thesis proposes several models to describe the behavior of the sensors, in order to hybridize them rigorously in the bundle-adjustment. Consistent datasets have been acquired on the field specifically to assess the relevance of both the sensor models and their hybridizing in complex photogrammetric processing.
The contribution of this thesis could be divided into two mains categories. On one hand, this thesis suggests tools and recommendation to improve directly the procedures achieved by end-users using current UAV-mapping commercial solutions (in particular for the GCPs placement, for the choice of the camera calibration and model and for the flight-plan). On the other hand, this thesis put forward exotic methods (methods considered as exotic at the time of the writing of the thesis) such as Photo-LIDAR hybridizing and collaborative mapping achieved by a terrestrial-aerial tandem (a terrestrial vehicle holding a LIDAR, GNSS, imaging and inertial sensors followed by a drone conceived to proceed to airborne photogrammetry) or an aerial-aerial tandem (two drones flying in formation to proceed to airborne photogrammetry).
The contribution of this thesis will permit to reduce costs, to improve the quality of mapping products and to enlarge the possibilities of mapping: in particular, map cluttered or inaccessible zones which are nowadays considered as difficult or even impossible to map.
Jan Skaloud, Davide Antonio Cucci, Kyriaki Mouzakidou
, ,