The Area Processing Unit of Caroline - Finding the Way through DARPA’s Urban Challenge
This paper presents a vision-based color segmentation algorithm suitable for urban environments that separates an image into areas of drivable and non-drivable terrain. Assuming that a part of the image is known to be drivable terrain, other parts of the image are classified by comparing the Euclidean distance of each pixel’s color to the mean colors of the drivable area in real-time. Moving the search area depending on each frame’s result ensures temporal consistency and coherence. Furthermore, the algorithm classifies artifacts such as white and yellow lane markings and hard shadows as areas of unknown drivability. The algorithm was thoroughly tested on the autonomous vehicle ’Caroline’, which was a finalist in the 2007 DARPA Urban Challenge.
Unable to display preview. Download preview PDF.
- 1.Thrun, S., et al.: Stanley: The Robot That Won The DARPA Grand Challenge, Journal of Field Robotics, 661–692 (2006)Google Scholar
- 2.Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis (1973)Google Scholar
- 3.Jeff, B.: A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models Technical Report, University of Berkeley, ICSI-TR-97-021 (1997)Google Scholar
- 4.Gary, B., Adrian, K., Vadim, P.: Learning-Based Computer Vision with Intels Open Source Computer Vision Library Intel Technology. Journal - Compute-Intensive, Highly Parallel Applications and Uses 9(2), 126–139 (2005)Google Scholar
- 5.Iwan, U., Illah, N.: Appearance-Based Obstacle Detection with Monocular Color Vision Proceedings of the AAAI National Conference on Artificial Intelligence, Austin, TX, 866–871 (2000)Google Scholar
- 6.Open Source Computer Vision Library, http://www.intel.com/research/mrl/research/opencv