Abstract
Abstract. In this paper we present a robust orientation approach for an imaging sensor flown on a micro-UAV based on image triplets. Our aim is to have the orientation available online, i.e. during image acquisition. The resulting point cloud and sensor orientations can then for instance be evaluated for navigation purposes of the UAV or to analyse the completeness of the point cloud. We use low quality imagery extracted from the downlink of an onboard PAL-camera. Trilinear constraints and cross-checked matches allow for a high robustness of the sensor orientation and the sparse 3D point cloud. In order to reach the goal of on-line processing given the large number of observations and unknowns, we make use of an incremental bundle adjustment. Estimated parameters are incrementally improved without explicitly considering previous observations. Our approach combines linear projective geometry for obtaining initial values using the trifocal tensor with non-linear perspective geometry for the estimation of the unknowns. This combination allows for a high precision of the estimation, while eliminating the need for initial values. We evaluate the performance of our approach by means of imagery we acquired of the facade of theWelfenschloss in Hannover, collected with a Microdrones md4-200 micro-UAV. The results are the orientation parameters of the images and a sparse 3D point cloud representing the object. They are compared to those from a commercial bundle adjustment software and analysed in terms of geometric precision.
Highlights
Unmanned Aerial Vehicles (UAV) provide a flexible instrument for many tasks in photogrammetry
For the first image triplet, we compute the trifocal tensor to obtain initial values for relative orientation (Hartley and Zisserman, 2000), (Ressl, 2000), which we use as an input for non-linear bundle adjustment to obtain the optimal estimate of the orientation parameters and the object coordinates of the tie points
We assumed the geometry of the camera and, the calibrated values of the interior orientation and the distortion parameters, to be constant during the flight and assigned these parameter values to all the collected images
Summary
Unmanned Aerial Vehicles (UAV) provide a flexible instrument for many tasks in photogrammetry. A building may be composed of courtyards or terraces, which may be unknown prior to data acquisition. If in such cases the initial flight path is not adequately refined on-the-fly once the additional detail becomes apparent during data acquisition, the result will be incomplete. In order not to miss relevant information about the object, an on-line adaptation of the flight path is required. Overlapping image triplets are used for robust keypoint matching and for determining initial values for the unknowns. The initial values of the unknown orientations for the first image triplet are obtained from the trifocal tensor, determined from triples of homologous points.
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