Region Extraction in Large-Scale Urban LIDAR Data

Region Extraction in Large-Scale Urban LIDAR Data
Topics in Machine Vision
Spring 2011
Alexandru Tandrau
alexandru@tandrau.com

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
Compute planarity measures
Grow regions
Combine regions
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
Approach 1
gist
gist

Grow Regions
Combine Regions
Prune Regions
Approach 2

ε-neighborhood with maximum planarity
Find points to add to the region
Update region’s planar approximation
repeat for other neighborhoods
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
Grow Regions
Combine Regions
Prune Regions

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 dataset
21.4 minutes, ± 80% accuracy.

A similar algorithm
[5]

?
Presentation and collection of links also available at http://www.tandrau.com/mv_seminar

References
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.
P. Flynn, A. Jain. Surface classification: hypothesis testing and parameter estimation. In Proceedings CVPR ‘88, pages 261-267, Jun 1988.
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.
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.
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.

Region Extraction in Large-Scale Urban LIDAR Data

by Alex Tandrau on Feb 28, 2011

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Region Extraction in Large-Scale Urban LIDAR Data — Presentation Transcript