Abstract
Abstract. Civil applications for small size unmanned aerial vehicles (UAV) have become quite important in recent years and so have accurate orientation and navigation of these devices in unknown terrain. In this work we focus on on-line compatible positioning in facade observation based on monocular low resolution still images acquired by a camera mounted on a UAV. Also, a 3D point cloud of the facade is generated. This allows further processing steps, e.g. navigation assistance, collision avoidance or the evaluation of the point cloud density, verifying completeness of the data. To be able to deal with the increasing amount of observations and unknown parameters we implement an incremental bundle adjustment based on automatically determined tie points and sliding image triplets. The tripletwise orientation allows for an efficient double cross-check of the detected feature points and hence guarantees reliable initial values for the nonlinear bundle adjustment. The initial values are estimated within a convex formulation delivering a sound basis for the incremental adjustment. Our algorithm is evaluated by means of imagery we took of the facade of the Welfenschloss in Hannover, captured from a manually flown Microdrones md4-200 micro-UAV.We compare the orientation results of our approach with an approach in which initial values for the unknown object coordinates are computed algebraically.
Highlights
Comprehensive information about facades is important e. g. for different fields like architecture, cultural heritage or the construction of realistic 3D city models
In this work we present a new incremental orientation approach based on image triplets, which is able to deliver the position and orientation of the unmanned aerial vehicles (UAV) and a sparse point cloud of the observed object in near real-time
Simultaneous georeferencing of images and 3D point cloud generation in real- or near real-time based on monocular imagery, which is called simultaneous localisation and mapping (SLAM), has been in the focus of several publications: On-line orientation and dense-matching implementations based on projective geometry alone can be found in (Klein and Murray, 2009) and (Wendel et al, 2012), respectively
Summary
Comprehensive information about facades is important e. g. for different fields like architecture, cultural heritage or the construction of realistic 3D city models. In this work we present a new incremental orientation approach based on image triplets, which is able to deliver the position and orientation of the UAV and a sparse point cloud of the observed object in near real-time. We use a combination of projective and perspective geometry The former is used to obtain initial values for the first image triplet. The latter forms the foundation of an incremental bundle adjustment. A previous version of this approach is described in (Reich et al, 2013), this paper contains two important extensions: Firstly, the estimation of initial values for the unknown object coordinates of the homologous feature points is formulated as a convex optimisation problem.
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