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Combining laser range, color, and texture cues for autonomous road following

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1 Author(s)
C. Rasmussen ; Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA

We describe results on combining depth information from a laser range-finder and color and texture image cues 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 as color histograms and Gabor filter responses. A small set of road models was 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. Segmented images combined with laser range information and the vehicle's inertial navigation data were used to construct 3-D maps suitable for path planning.

Published in:

Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on  (Volume:4 )

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