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 the Welfenschloss 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.