A new approach for matching surfaces from laser scanners and optical sensors
Surfaces play an important role in diverse applications, such as orthophoto production, city modeling, ice sheet monitoring, and object recognition. Surfaces are usually obtained by a sampling process. The raw sampled data must be processed further. A frequently occurring task is the comparison of t...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Published: |
1999
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.3382 http://www.isprs.org/proceedings/XXXII/3-W14/pdf/p55.pdf |
| Summary: | Surfaces play an important role in diverse applications, such as orthophoto production, city modeling, ice sheet monitoring, and object recognition. Surfaces are usually obtained by a sampling process. The raw sampled data must be processed further. A frequently occurring task is the comparison of two surfaces. In the most general case, the two surfaces are described by discrete sets of points, whereby the point density may be different as well as the reference systems. We propose to compare two surfaces by computing the shortest distance between points in one surface and locally interpolated surface patches of the second surface. This entails a correspondence between points and surface patches. We describe a solution to this matching problem that is based on a parameter space representation. After a brief problem statement we explain the proposed matching scheme by way of an example. We then apply the method to determine the transformation parameters between the two surfaces. To arrive at an operational solution, we reduce the n-parameter space to one dimension by an iterative solution. The feasibility of our matching scheme is demonstrated with simulated data sets as well as real data. We show how a surface determined by laser scanning can be compared with the same physical surface but established by photogrammetry. As a natural extension, one can use the method for change detection. 1 |
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