Region Extraction in Large-Scale Urban LIDAR Data

Topics in Machine VisionSpring 2011

Alexandru Tandraualexandru@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

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.