Region Extraction in Large-Scale Urban LIDAR Data
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Region Extraction in Large-Scale Urban LIDAR Data Topics in Machine Vision Spring 2011 Alexandru Tandrau alexandru@tandrau .com
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Seminar Presentation @Jacobs University, 02 March 2011. Based on A. Zavodny, P. Flynn, X. Chen. Region Extraction in Large-Scale Urban LIDAR Data. In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pages 1801-1808.
Transcript of Region Extraction in Large-Scale Urban LIDAR Data
Region Extraction in Large-Scale Urban LIDAR Data
Topics in Machine VisionSpring 2011
Alexandru [email protected]
Input: Large 3D point cloud of urban environment, acquired by moving vehicles equipped with SICK LIDAR scanners, GPS-registered.
Output: Point cloud labeling by identifying planar regions
NAVTEQ
Beauvais Cathedral
Algorithm Overview
1. Compute planarity measures2. Grow regions3. Combine regions4. Prune regions
Best Fitting Plane
• Covariance Matrix gist
• Compute Eigenvalues• Compute Eigenvector v for smallest eigenvalue• Equation of a plane• Identify arguments of plane equation with
eigenvector gist
Tip The GSL (Gnu Scientific Library) contains, among others, eigenvalue/eigenvector libraries for C++.
Compute Planarity Measures
Grow Regions
Combine Regions
Prune Regions
€
PM(Ps) =distToPlane(Pi,F)i
P
∑P
Approach 1
gist
gist
Compute Planarity Measures
Grow Regions
Combine Regions
Prune Regions
Approach 2
€
Navg =N ii
N
∑N
€
PM(Ps) =angleBetween(Navg,N i)
2
i
N
∑N
• ε-neighborhood with maximum planarity
– Find points to add to the region
– Update region’s planar approximation
• repeat for other neighborhoods
Compute Planarity Measures
Grow Regions
Combine Regions
Prune Regions
• Regions Ri and Rj describe same surface if:– Normals of regions point in similar directions (Tnra)– The regions are close together
• Identify connected components on the graph formed by valid region pairs
Compute Planarity Measures
Grow Regions
Combine Regions
Prune Regions
€
d(Ri,R j ) = min∀p i ∈R i ,p j ∈R j
(dist(pi, p j ))
Compute Planarity Measures
Grow Regions
Combine Regions
Prune Regions
Prune regions whose average planarity measure is above a threshold Tapm.
Thresholds
• Neighborhood definitions– Epsilon– K
• Growing Regions– Trd
– Tpd
• Combining Regions– Tnra
– Tnrd
• Prunning Regions– Tapm
Data Acquisition
point = (latitude, longitude, elevation)
Experimental Results
Campus dataset, 95 mil. range-scanned points
Split data into subsets (based on acquire timestamp), process subsets independently
Execution time for the entire dataset21.4 minutes, ± 80% accuracy.
A similar algorithm
[5]
?
Presentation and collection of links also available at http://www.tandrau.com/mv_seminar
References1. A. Zavodny, P. Flynn, X. Chen. Region Extraction in Large-Scale Urban LIDAR Data. In 2009
IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pages 1801-1808.
2. P. Flynn, A. Jain. Surface classification: hypothesis testing and parameter estimation. In Proceedings CVPR ‘88, pages 261-267, Jun 1988.
3. C. C. Chen and I. Stamos. Range image segmentation for modeling and object detection in urban scenes. In Proc. 3DIM ’07, pages 185-192, 2007.
4. P. K. Allen, A. Troccoli, B. Smith, I. Stamos, S. Murray. The Beauvais cathedral project. In Proc. Computer Vision and Pattern Recognition Workshop CVPRW ’03, volume 1, pages 10-10, June 2003.
5. J. Poppinga, N. Vaskevicius, A. Birk, K. Pathak. Fast Plane Detection and Polygonalization in noisy 3D Range Images. In 2008 IROS International Conference on Intelligent Robots and Systems.
