Here is a paper about integration techniques:
NEW INTEGRATION APPROACH OF PHOTOGRAMMETRIC AND LIDAR
TECHNIQUES FOR ARCHITECTURAL SURVEYS
F. Nex, F. Rinaudo
"ABSTRACT:
In the last few years, LIDAR and image-matching techniques have been employed in many application fields because of their quickness in point cloud generation. Nevertheless, these techniques do not assure complete and reliable results, especially in complex applications such as architectural surveys: laser scanning techniques do not allow the correct position of object breaklines to be extracted while image matching results require an accurate editing and they are not acceptable for bad-textured images. For this reason several authors have already suggested overcoming of these problems through a combination of LIDAR and photogrammetric techniques. These works considers the integration as the possibility to share point clouds generated by these techniques; however, a complete and automatic integration, in order to achieve more complete information, has never been implemented. In this paper, a new integration approach is proposed. This integration is focused on the possibility of overcoming the problems of each technique. In this approach LIDAR and multi-image matching techniques combine and share information in order to extract building breaklines in the space, perform the point cloud segmentation and speed up the modelling process in an automatic way. This integration is still an ongoing process: the algorithm workflow and first performed tests on real facades are presented in this paper, in order to evaluate the reliability of the proposed method; finally, an overview on the future developments is offered.
CONCLUSIONS AND FUTURE DEVELOPMENTS
The performed tests have allowed a first evaluation to be made of the potentiality of the proposed method, even though this analysis is not complete yet and further tests and changes have to be defined. Nevertheless, the results have already shown the reliability of the algorithm. In general, the results depend on the image taking configuration: almost normal case images are weak in the matching of edges parallel to epipolar lines. This problem could be overcome by just using more than three images and an ad hoc taking geometry. Images acquired at different heights allow epipolar lines with different direction to be obtained (Figure 9); instead, convergent images (more than 20°) could be subject to problems due to affine deformations. The taking distance should be chosen according to the degree of detail requested in the survey: in general, a 15 m distance can be considered the maximum for architectural objects to be drawn at 1:50 scale. The algorithm has shown that it can achieve good results for repetitive patterns, particularly if more than three images are used. The number of mismatches is usually low and decreases as the number of images increases. Glass, however, must always be deleted from all the images, in order to avoid mismatches. Dense point clouds in the LIDAR acquisition are not strictly necessary during the matching process while they are necessary instead in the filtering of the geometric edges. In fact the algorithm has shown some problems because of the presence of shadows close to the geometric breaklines; a more dense point cloud could overcome this problem. Furthermore, rounded edges are difficult to model as it is difficult to identify the position of the breakline in the image: in this situation, the algorithm does not allow good results to be obtained. It is expected that the geometric precision in the edge matching will be increased by implementing a Least Square Matching (LSM). A first step will be to perform a Multi-Photo LSM of dominants points; then it is planned to carry out an LS B-Snake matching (Zhang, 2005) which could slightly improve the quality of extracted edges. A great advantage in the traditional point cloud segmentation will be obtained from the extracted edges. The segmentation will be guided by edges that define the boundaries of each façade portion and fix a constraint in the region growing algorithm. In this way it is expected to make the modelling procedures easier."
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