A Practical Solution: GOLD
Generic
Obstacle
• Theoretical Bases
• Lane Detection
• Obstacle Detection
• Strong Points
• Weak Points
and Lane
Detection
Theoretical Bases
The Inverse Perspective Mapping
The 3d space is transformed into the image space by the
Perspective Transformation. The coordinates in the 3d
space are (x,y,z), and the (u,v) are the coordinates in the
image space.
Theoretical… (continued)
Mapping from image to a z=0 plane in 3d world (IPM)
Mapping from the 3d space to image space (remapping)
Theoretical…(example)
Camera parameters which are relevant in the computation
Example of a result obtained from IPM. At right, the FOV of the camera
Lane Detection
a)
b)
c)
a)
Original Image
b)
IPM image
c)
Filtered image
d)
Enhanced image (using morfodilatation)
e)
Binarized image
d)
e)
Obstacle Detection
Stereo IPM
Main Idea: the Zero Disparity Surface (HOROPTER)
Cameras are calibrated so that the IPM images from both cameras
will be identical in the features aquired from the road plane (the
horopter is a line, not a curve)
Obstacle Detection (cont’d)
a)
a)
b)
c)
d)
b)
c)
d)
Acquired images from the left and right cameras
IPM transformed images (L,R)
Difference image. (Obstacles are the ones causing the differences)
Remapped image. A black line indicates the detected obstacle
The Computing Architecture
The PAPRICA System
PArallel PRocessor for Image Checking and Analysis
Strong Points
- Tested on MOBLAB vehicle, on extra-urban roads, for 3000km
- The vehicle speed: up to 80km/h
- Not influenced by shadows on the road, illumination conditions
and road&vehicle texture
Weak Points
Lane detection fails when: - the road is not flat (a,b)
- the road markings are not visible (c,d)
Due to the vehicle movements slight changes appear in the camera
parameters – this problem can be solved by image stabilization schemes