Fathi, H and Brilakis, I (2011) Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features. Advanced Engineering Informatics, 25. pp. 760-770. ISSN 1474-0346Full text not available from this repository.
The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.
|Uncontrolled Keywords:||Spatial data collection Videogrammetry Sparse point cloud SURF features Automatic point matching Passive remote sensing|
|Depositing User:||Cron job|
|Date Deposited:||16 Jul 2015 13:08|
|Last Modified:||02 Aug 2015 01:15|