Stanford researchers were the winners of the Defense Advanced Research Projects Agency's (DARPA's) Grand Challenge not long ago. They are now preparing their new vehicle for the upcoming Urban Challenge. We have talked about the new challenge in previous posts. Currently, DARPA is visiting qualified teams and testing their vehicles during a second qualifying round as they select only the best teams for the final event to take place later this year; there are currently 53 teams that are still in the competition. DARPA visited Stanford's team last week and tested their autonomous passenger VW car which as expected passed with flying colors; Stanford Racing Team's robotic car passed 3 of the 4 tests given during a 2.5-hour long course in a parking lot near Google headquarters in California. You can watch a video of the robot trial at the San Fransisco Chronicle website here.
One interesting aspect of the Stanford car is that other than using several laser sensors to judge the distance to other vehicles on the road, the car also has a spherical vision camera mounted on the roof. The camera is a Point Grey Research spherical vision Ladybug 2. As you can see from the image to the right, the Ladybug consists of 6 CCD cameras in a small package capable of simultaneously capturing images that cover 75% of the visible sphere. The camera connects to a computer using an IEEE-1394b interface allowing the transfer of data to the computer at 30fps. In case you are wondering what spherical vision images look like after all individual images (from each camera) are stitched together then watch the following video,
The video is provided by Point Grey Research and can be found on a demo DVD that can be requested from the company via its sales team.
So, I wonder what does Stanford's Junior use the data from the Ladybug for? The Stanford team may be using the visual data for doing mapping and localization when the GPS is failing. Or the visual data may be used to detect and recognize road signs and traffic lights. The latter is one component of the Urban Challenge and it would be very hard if not impossible to solve using a time-of-flight sensor. It would also be a bit more expensive to construct a specialized sensor just for detecting traffic lights and road signs.
I am really looking forward to the Urban Challenge competition this coming November.
4 comments:
7:56 AM
I think your guess is right. What I think it's doing is combining the 3D models built by the laser scanners with the colour omnidirectional vision data to build a full colour 3D model of the surroundings. From this it will then be able to identify road signs and read them.
I'll be interested to see the results of the Urban Challenge. Navigating in traffic whilst obeying all the rules of the road is certainly a hard AI problem and there are many ways in which the system could fail. Also navigating on busy streets containing people and other vehicles will be quite different from navigating around a few traffic cones on an empty carpark.
10:16 AM
"Or the visual data may be used to detect and recognize road signs and traffic lights."
That isn't part of the challenge.
There are stop lines that are given to the teams as high accuracy GPS positions. That is about the only traffic sign.
They're probably doing static vehicle detection, moving obstacle detection, obstacle extenct calculation, traversable road classification, and lane-line detection.
4:10 PM
Thanks for the overview! Classy writing as always.
8:46 PM
@bob, I agree with you that this is a difficult AI problem and the actual competition in November will be very interesting.
@ivan, Thanks for correcting me; I should have reviewed the rules once more before posting.
@hthth, Thanks for the good words!
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