Abstract
Abstract. The growing deployment of multi-head camera systems encouraged the emergence of specific processing algorithms, able to face the challenges posed by slanted view geometry. Such multi-camera systems are rigidly tied by their manufacturers hence the exploitation of this internal constraint should be further exploited. Several approaches have been proposed to deal with orientation constraints, with the aim of reducing the number of unknowns, computational time and possibly improve the accuracy. In this paper we compare the results provided by publicly available implementations in order to further investigate the advantages of enforcing relative orientation constraints for aerial and terrestrial triangulation of multi-head camera systems. Data from a Leica CityMapper and a Stereopolis-Ladybug are considered, reporting how constrained solution can improve accuracy with respect to traditional (unconstrained) bundle block adjustment solutions.
Highlights
In the last years, most existing companies in the geospatial industry have embraced multi-camera oblique imaging technology thereby expanding the potential of the area-wide mapping market
Multi-head camera systems provide the advantages of slanted view geometry, which allows for the 3D reconstruction of building facades and other vertical objects (Haala and Rothermel, 2015)
Regarding the image orientation problem, several works in the literature suggest that relative orientation constraints among the cameras should be considered (Wiedemann and More, 2012, Rupnik et al, 2015), in order to reduce the number of unknowns and possibly to improve the accuracy
Summary
Most existing companies in the geospatial industry have embraced multi-camera oblique imaging technology thereby expanding the potential of the area-wide mapping market. Multi-head camera systems provide the advantages of slanted view geometry, which allows for the 3D reconstruction of building facades and other vertical objects (Haala and Rothermel, 2015) This poses new challenges, which include dealing with image scale variability, multiple occlusions, and greater disparity in search space. Regarding the image orientation problem, several works in the literature suggest that relative orientation constraints among the cameras should be considered (Wiedemann and More, 2012, Rupnik et al, 2015), in order to reduce the number of unknowns and possibly to improve the accuracy In this regard, two main approaches have been proposed to deal with orientation constraints:
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