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Download fileSensor and Classifier Fusion for Outdoor Obstacle Detection: an Application of Data Fusion To Autonomous Off-Road Navigation
journal contribution
posted on 2003-01-01, 00:00 authored by Cristian S. Dima, Nicolas Vandapel, Martial HebertThis paper describes an approach for using several levels
of data fusion in the domain of autonomous off-road
navigation. We are focusing on outdoor obstacle detection,
and we present techniques that leverage on data fusion and
machine learning for increasing the reliability of obstacle
detection systems.
We are combining color and IR imagery with range information
from a laser range finder. We show that in addition
to fusing data at the pixel level, performing high level
classifier fusion is beneficial in our domain. Our general
approach is to use machine learning techniques for automatically
deriving effective models of the classes of interest
(obstacle and non-obstacle for example). We train classifiers
on different subsets of the features we extract from
our sensor suite and show how different classifier fusion
schemes can be applied for obtaining a multiple classifier
system that is more robust than any of the classifiers presented
as input.
We present experimental results we obtained on data
collected with both the Experimental Unmanned Vehicle
(XUV) and a CMU developed robotic vehicle.