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Combining Laser Range, Color, and Texture Cues for Autonomous Road Following

Published

Author(s)

C E. Rasmussen

Abstract

We describe preliminary results on combining depth information from a laser range-finder and color and texture image cues to train classifiers to segment ill-structured dirt, gravel, and asphalt roads as input to an autonomous road following system. A large number of registered laser and camera images were captured at frame-rate on a variety of rural roads, allowing laser features such as 3-D height and smoothness to be correlated with image features such color histograms and Gabor filter responses. A small set of road models were generated by training separate neural networks on labeled feature vectors clustered by road ``type.'' By first classifying the type of a novel road image, an appropriate second-stage classifier was selected to segment individual pixels, achieving a high degree of accuracy on arbitrary images from the dataset.
Proceedings Title
Proc. IEEE Inter. Conf. on Robotics & Automation
Conference Dates
May 1, 2002
Conference Location
Washington DC, MD
Conference Title
Robotics & Automation

Keywords

classification, color histogram, Gabor filter, k-means clustering, laser range finder, neural network, road following, road segmentation

Citation

Rasmussen, C. (2002), Combining Laser Range, Color, and Texture Cues for Autonomous Road Following, Proc. IEEE Inter. Conf. on Robotics & Automation, Washington DC, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=821641 (Accessed May 27, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created May 1, 2002, Updated February 17, 2017