The Robotics Institute

Carnegie Mellon Robotics Institute

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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

Ian 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",
   booktitle = "",
   institution = "Robotics Institute",
   month = "January",
   year = "1995",
   number= "CMU-RI-TR-95-05",
   address= "Pittsburgh, PA",
}

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