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"Outlier detection of point clouds generating from low-cost UAVs for bridge inspection" presented at IALCCE2018 by Siyuan Chen

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Using an Unmanned Aerial Vehicle (UAV) for documentation and inspection of civil infrastructures has become increasingly popular. One area of interest is in bridge inspection as it holds the potential of being safer, more economical, and less disruptive, with respect to traffic flow. With 3D reconstruction method, structural deficiencies and 3D models can be obtained from a 3D point cloud generated from UAV imagery data. However, shadows and water reflectivity may affect the quality of the point cloud generated from images, which causes difficulty in data processing. This paper presents a detailed workflow of removing outlier data points through the statistical filter and geometric-based filter. The experimental results showed that the statistical filter gives the best performance.

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"Outlier detection of point clouds generating from low-cost UAVs for bridge inspection" presented at IALCCE2018 by Siyuan Chen

  1. 1. Reducing Uncertainty in Structural Safety Special Session SS6 Ghent, Belgium 28-31 October 2018
  2. 2. Siyuan Chen, Linh Truong-Hong and Debra Laefer Outlier detection of point clouds generating from low cost UAVs for bridge inspection
  3. 3. Traditional Method TLS Survey Vehicle
  4. 4. UAV Inspection UAV System UAV Survey
  5. 5. Flowchart for Data Processing
  6. 6. Image Acquisition
  7. 7. 3D Reconstruction Structure From Motion (SFM) Images form: Prof. Rob Fergus, Computer science, NYU
  8. 8. Noise Reduction Processed by statistical filter (K=400, t=0) Processed by geometric-based filter (K=20, r=0.05)
  9. 9. Noise Reduction Aerial image Point density distribution Dataset prior to denoising Cleared Dataset
  10. 10. Bridge Deck Extraction k-means Region growing
  11. 11. Pavement Evaluation
  12. 12. The TRUSS ITN project (http://trussitn.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642453 Thanks for your attention

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