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RI | Publications | Sensor Fusion for Autonomous Outdoor Navigation Using Neural Networks
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Text only version of this site
Sensor Fusion for Autonomous Outdoor Navigation Using Neural Networks
I. Davis
tech. report CMU-RI-TR-95-05, Robotics Institute, Carnegie Mellon University, January, 1995.
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| Abstract |
For many navigation tasks, a single sensing modality is sufficiently rich to accomplish the desired motion control goals; for practical autonomous outdoor navigation, a single sensing modality is a crippling limitation on what tasks can be undertaken. In the research detailed in this paper, we open the door for a whole new suite of real-time autonomous navigation tasks previously unattainable. Using neural networks, including a neural network paradigm particularly well suited to sensor fusion, and Carnegie Mellon University's HMMWV off-road vehicle, we have successfully performed simulated and real-world navigation tasks that required the use of multiple sensing modalities.
| Notes |
Sponsor: NSF Graduate Research Fellowship
Grant ID: DACA76-89-C0014, DAAE07-90-C-R059
Number of pages: 23
| Text Reference |
I. Davis, Sensor Fusion for Autonomous Outdoor Navigation Using Neural Networks, tech. report CMU-RI-TR-95-05, Robotics Institute, Carnegie Mellon University, January, 1995.
| BibTeX Reference |
@techreport{Davis_1995_364,
author = "Ian Davis",
title = "Sensor Fusion for Autonomous Outdoor Navigation Using Neural Networks",
institution = "Robotics Institute, Carnegie Mellon University",
month = "January",
year = "1995",
number = "CMU-RI-TR-95-05",
address = "Pittsburgh, PA"
}