Abstract
Abstract. We have recently released in the open domain data originating from a series of flights conducted with a fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications. However, the data that we describe in the following is part of a larger series that will be released gradually after incorporating user feedback (e.g., on formats, description, etc.) from the first (three) released open data-sets. In the first part of this work we provide a thorough description of the common elements of these data sets, notably the UAV, its sensors, methods of time-stamping and synchronizing data streams, reference geometrical relations among them (system calibration) as well as time-invariant sensor parameters (e.g., lens distortion, non-orthogonality of inertial sensors) together with ground control points that are valid over the whole series. In the second part we describe the individual missions and provide the links to the released data sets.
Highlights
1.1 MotivationThis paper introduces a new reference / benchmark data set for evaluating of UAV mapping solution
Coordinates of its phase-center are tied by post-processing to the Automated GNSS Network of Switzerland (AGNES)1
Tab. 6 provides an overview of exploitable missions flown in the period May - October 2018 together with their specifications: trajectory shape, surface covered in hectares, mean altitude above ground level (AGL), ground sampling distance (GSD)/resolution of the imagery and status of data
Summary
This paper introduces a new reference / benchmark data set for evaluating of UAV mapping solution. On one side, using a drone as a payload platform does not change the wellestablished principles of geo-referencing optical data as they are principally independent of the vehicle holding the instruments; on the other hand, the quality of on-board devices is limited by the reduced size that is required for their use with drones This applies to both navigation and optical sensors which in turn affects the mapping performance. At the same time it provides an opportunity to revise, modify or refine some theoretical aspects of photogrammetry and their applications in terms of modeling and sensor data fusion For such development, the access of “raw sensor data” along with reference/bench-marking values (either in sensor or spatial domain) is very important, yet not so easy to obtain. Supporting such evolution is the motivating factor behind this publication
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