This work has been performed in conjunction with the University of Detroit Mercy's (UDM) ECE Department autonomous vehicle entry in the 2006 Intelligent Ground Vehicle Competition (www.igvc.org). The IGVC challenges engineering students to design autonomous vehicles and compete in a variety of unmanned mobility competitions. The course to be traversed in the competition consists of a lane demarcated by painted lines on grass with the possibility of one of the two lines being deliberately left out over segments of the course. The course also consists of other challenging artifacts such as sandpits, ramps, potholes, and colored tarps that alter the color composition of scenes, and obstacles set up using orange and white construction barrels. This paper describes a composite lane edge detection approach that uses three algorithms to implement noise filters enabling increased removal of noise prior to the application of image thresholding.
The first algorithm uses a row-adaptive statistical filter to establish an intensity floor followed by a global threshold based on a reverse cumulative intensity histogram and a priori knowledge about lane thickness and separation. The second method first improves the contrast of the image by implementing an arithmetic combination of the blue plane (RGB format) and a modified saturation plane (HSI format). A global threshold is then applied based on the mean of the intensity image and a user-defined offset. The third method applies the horizontal component of the Sobel mask to a modified gray scale of the image, followed by a thresholding method similar to the one used in the second method.
The Hough transform is applied to each of the resulting binary images to select the most probable line candidates. Finally, a heuristics-based confidence interval is determined, and the results sent on to a separate fuzzy polar-based navigation algorithm, which fuses the image data with that produced by a laser scanner (for obstacle detection).
This work has been performed in conjunction with the ECE Department's autonomous vehicle entry in the 2006 Intelligent Ground Vehicle Competition (www.igvc.org). The course to be traversed in the competition consists of a lane demarcated by paint lines on grass along with other challenging artifacts such as a sandpit, a ramp, potholes, colored tarps, and obstacles set up using orange and white construction barrels. In this paper an enhanced obstacle detection and mapping algorithm based on region-based color segmentation techniques is described.
The main purpose of this algorithm is to detect obstacles which are not properly identified by the LADAR (Laser Detection and Ranging) system optimally mounted close to the ground, due to "shadowing" occasionally resulting in bad navigation decisions. On the other hand, the camera that is primarily used to detect the lane lines is mounted at 6 feet. In this work we concentrate on the identification of orange/red construction barrels. This paper proposes a generalized color segmentation technique which is potentially more versatile and faster than traditional full or partial color segmentation approaches. The developed algorithm identifies the shadowed items within the camera's field of vision and uses this to complement the LADAR information, thus facilitating an enhanced navigation strategy. The identification of barrels also aids in deleting bright objects from images which contain lane lines, which improves lane line identification.
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