Recently, various kinds of vector data have been widely used. Images as raster data also became popular, and then applications using the vector data and images simultaneously attract more interests. Such applications require registration of those data in a same coordinates system. This paper proposes an orientation method combining the vector data with the images based on bundle adjustment. Since the vector data can be regarded as constraint condition, the bundle adjustment is extended to constrained non-linear optimization method. The constraint conditions are coincidence between lines extracted from images and the corresponding ones of vector data. For formulation, a representative point is set as midpoint of a projected line of vector data on the image. By using the representative points, the coincidence condition is expressed as distance the point and the lines extracted from the image. According to the conditions, the proposed method is formulated as Lagrange's method of undetermined multipliers. The proposed method is applied to synthetic and real data (compared with laser scanner data). The experiments with both synthetic and real data show that the proposed method is more accurate to errors caused by low accuracy of coordinates of feature points than a method without constraint conditions. According to the experiments, the significance of the proposed method is confirmed.

Date Published: 21 June 2015
PDF: 8 pages
Proc. SPIE 9528, Videometrics, Range Imaging, and Applications XIII, 95280B (21 June 2015); doi: 10.1117/12.2184544

Published in SPIE Proceedings Vol. 9528:
Videometrics, Range Imaging, and Applications XIII
Fabio Remondino; Mark R. Shortis, Editor(s)