Method of the Determination of Exterior Orientation of Sensors in Hilbert Type Space

Abstract

The following article presents a new isometric transformation algorithm based on the transformation in the newly normed Hilbert type space. The presented method is based on so-called virtual translations, already known in advance, of two relative oblique orthogonal coordinate systems—interior and exterior orientation of sensors—to a common, known in both systems, point. Each of the systems is translated along its axis (the systems have common origins) and at the same time the angular relative orientation of both coordinate systems is constant. The translation of both coordinate systems is defined by the spatial norm determining the length of vectors in the new Hilbert type space. As such, the displacement of two relative oblique orthogonal systems is reduced to zero. This makes it possible to directly calculate the rotation matrix of the sensor. The next and final step is the return translation of the system along an already known track. The method can be used for big rotation angles. The method was verified in laboratory conditions for the test data set and measurement data (field data). The accuracy of the results in the laboratory test is on the level of 10−6 of the input data. This confirmed the correctness of the assumed calculation method. The method is a further development of the author’s 2017 Total Free Station (TFS) transformation to several centroids in Hilbert type space. This is the reason why the method is called Multi-Centroid Isometric Transformation—MCIT. MCIT is very fast and enables, by reducing to zero the translation of two relative oblique orthogonal coordinate systems, direct calculation of the exterior orientation of the sensors.

Method of Improving Incomplete Spatial-Temporal Data in Inland Navigation, on the Basis of Industrial Camera Images – West Oder River Case Study

“…Euler, Tait-Brian as well as quaternions (Titterton and Weston, 2004;Diebel, 2006;Allgeuer and Behnke, 2018). Special applications of transformation by similarity (1) (also for big rotation angles) take place, among others, in the transformation of point clouds (Brazeal, 2013), sensor orientation (Stępień, 2018), and in 3D modelling accuracy assessment (Brazetti and Scaioni, 2009).…”