Obstacle detection using v-disparity image

Based on: Global Correlation Based Ground Plane Estimation Using V-Disparity Image, By: J. Zhao, J. Katupitiya and J. Ward, 2007

Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation, By: A. Broggi, C. Caraffi, R. I. Fedriga, P. Grisleri, 2005

Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through ”V-disparity” Representation, By: R. Labayrade, D. Aubert, J. Tarel, 2002

Presented by: Ali Agha

March 25, 2009

Motivation

Obstacle avoidance using stereo vision Points with Z > 0

The pitch angle between the cameras and the road surface will change. Therefore, we need to compute the pitch angle and disparity of ground pixels dynamically

Related work

Disparity map 3D point cloud Ground plane extraction using plane fitting obstacle detection

Yu et al (2005)

Disparity map Optical flow and ground information obstacle detection

Mascarenhas (2008) Giachetti et al (1998)

Disparity map V-disparity image and ground information obstacle detection

Labayrade (2002) Broggi (2005) Zhao (2007)

V-Disparity image

Disparity map IΔ has been computed

IvΔ is built by

accumulating the

pixels of same

disparity in IΔ along

the v axis

d

vv

u

The image of a plane in v-disparity image

pin-hole camera model

Ground correlation line

Ideal and real road and obstacle

Hough Transform

Labayrade uses Hough transform to extract the ground

correlation line

free space on the road

Robust ground correlation extraction

Broggi et al. experimentally, found that the ground correlation line during a pitch variation oscillates, parallel to itself.

Zhao et al. investigated this characteristic mathematically and gave the condition for this characteristic to be valid.

Condition derived by Zhao et al

Θs= is 7.73.

Result of this condition

If this condition is satisfied.

GLOBAL CORRELATION METHOD

Exploiting this characteristic Fast and Robust method (even in lack of distinct features)

By accumulating the matching cost (intensity of V-disparity) along each of the candidate lines and choose the one with least (most) accumulated matching cost as ground correlation line.

Verifying equations and assumptions(Road has distinct features)

Implementing the Labayrade’s work

Disparity map V-D Hough

Verifying equations and assumptions(Road has distinct features)

In the sequence of images the position of ground correlation lines is

ranging from 25 to 28. The slop(g) of ground

correlation lines

ranges from 5.5 to

5.5652.

Results Applying the method on the same images

Calculate the matching cost (associated with each candidate ground correlation lines)

matching costs associated with candidate

lines

Same as Hough

transform

GCL

Without distinct features

Disparity map V-D Hough

Without distinct features - Comparison

Hough transform and global correlation method

Hough GCL

Obstacle detection

Conclusion

This accuracy is dependant of the image quality and whether or not the ground pixel dominate this area of the image.

It seems an appropriate method for detecting obstacles such as vehicles in road in night using structured light (as the headlight of automobile)

Thank you

Questions??