Automatic sensor orientation using horizontal and vertical line feature constraints

Abstract

To improve the accuracy of sensor orientation using calibrated aerial images, this paper proposes an automatic sensor orientation method utilizing horizontal and vertical constraints on human-engineered structures, addressing the limitations faced with sub-optimal number of Ground Control Points (GCPs) within a scene. Related state-of-the-art methods rely on structured building edges, and necessitate manual identification of end points. Our method makes use of line-segments but eliminates the need for these matched end points, thus eliminating the need for inefficient manual intervention.To achieve this, a 3D line in object space is represented by the intersection of two planes going through two camera centers. The normal vector of each plane can be written as a function of a pair of azimuth and elevations angles. The normal vector of the 3D line can be expressed by the cross product of these two plane’s normal vectors. Then, we create observation functions of horizontal and vertical line constraints based on the zero-vector cross-product and the dot-product of the normal vector of the 3D lines. The observation functions of the horizontal and vertical lines are then introduced into a hybrid Bundle Adjustment (BA) method as constraints, including observed image points as well as observed line segment projections. Finally, to assess the feasibility and effectiveness of the proposed method, simulated and real data are tested. The results demonstrate that, in cases with only 3 GCPs, the accuracy of the proposed method utilizing line features extracted automatically, is increased by 50%, compared to a BA using only point constraints.