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.

1. Reducing Uncertainty in Structural Safety
Special Session SS6
Ghent, Belgium
28-31 October 2018

2. Siyuan Chen, Linh Truong-Hong and Debra Laefer
Outlier detection of point
clouds generating from low
cost UAVs for bridge
inspection

3. Traditional Method
TLS Survey Vehicle

4. UAV Inspection
UAV System UAV Survey

5. Flowchart for Data Processing

6. Image Acquisition

7. 3D Reconstruction
Structure From Motion (SFM)
Images form： Prof. Rob Fergus, Computer science, NYU

8. Noise Reduction
Processed by statistical filter
(K=400, t=0)
Processed by geometric-based filter
(K=20, r=0.05)

9. Noise Reduction
Aerial image
Point density distribution
Dataset prior to denoising
Cleared Dataset

10. Bridge Deck Extraction
k-means
Region growing

11. Pavement Evaluation

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