|This 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.
|data fusion, sensor fusion, obstacle detection, off-road navigation|
Sponsor: U. S. Army Research Laboratory
Associated Center(s) / Consortia: Vision and Autonomous Systems Center, National Robotics Engineering Center, and Field Robotics Center
Associated Project(s): CTA Robotics and Vehicle Safeguarding
Number of pages: 8
|Cristian Dima, Nicolas Vandapel, and Martial Hebert, "Sensor and Classifier Fusion for Outdoor Obstacle Detection: an Application of Data Fusion To Autonomous Off-Road Navigation," The 32nd Applied Imagery Recognition Workshop (AIPR2003), October, 2003, pp. 255 - 262.|
author = "Cristian Dima and Nicolas Vandapel and Martial Hebert",
title = "Sensor and Classifier Fusion for Outdoor Obstacle Detection: an Application of Data Fusion To Autonomous Off-Road Navigation",
booktitle = "The 32nd Applied Imagery Recognition Workshop (AIPR2003)",
pages = "255 - 262",
publisher = "IEEE Computer Society",
month = "October",
year = "2003",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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