Real-time adaptive o-road vehicle navigation and terrain classification

Urs A. Muller; Lawrence D. Jackel; Yann Lecun; Beat Flepp

doi:10.1117/12.2015533

Real-time adaptive o-road vehicle navigation and terrain classification

Urs A. Muller, Lawrence D. Jackel, Yann Lecun, Beat Flepp

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au- Tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.

Original language	English (US)
Title of host publication	Unmanned Systems Technology XV
DOIs	https://doi.org/10.1117/12.2015533
State	Published - 2013
Event	Unmanned Systems Technology XV Conference - Baltimore, MD, United States Duration: May 1 2013 → May 3 2013

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	8741
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Other

Other	Unmanned Systems Technology XV Conference
Country/Territory	United States
City	Baltimore, MD
Period	5/1/13 → 5/3/13

Keywords

Continuous real-time learning
Intelligent systems
Machine learning
Off-road autonomous vehicle navigation
Self learning system
Sharing learned knowledge between systems
Vision-based passive long range sensing

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2015533

Cite this

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title = "Real-time adaptive o-road vehicle navigation and terrain classification",

abstract = "We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au- Tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.",

keywords = "Continuous real-time learning, Intelligent systems, Machine learning, Off-road autonomous vehicle navigation, Self learning system, Sharing learned knowledge between systems, Vision-based passive long range sensing",

author = "Muller, {Urs A.} and Jackel, {Lawrence D.} and Yann Lecun and Beat Flepp",

year = "2013",

doi = "10.1117/12.2015533",

language = "English (US)",

isbn = "9780819495327",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

booktitle = "Unmanned Systems Technology XV",

note = "Unmanned Systems Technology XV Conference ; Conference date: 01-05-2013 Through 03-05-2013",

}

TY - GEN

T1 - Real-time adaptive o-road vehicle navigation and terrain classification

AU - Muller, Urs A.

AU - Jackel, Lawrence D.

AU - Lecun, Yann

AU - Flepp, Beat

PY - 2013

Y1 - 2013

N2 - We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au- Tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.

AB - We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au- Tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.

KW - Continuous real-time learning

KW - Intelligent systems

KW - Machine learning

KW - Off-road autonomous vehicle navigation

KW - Self learning system

KW - Sharing learned knowledge between systems

KW - Vision-based passive long range sensing

UR - http://www.scopus.com/inward/record.url?scp=84881151466&partnerID=8YFLogxK

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U2 - 10.1117/12.2015533

DO - 10.1117/12.2015533

M3 - Conference contribution

AN - SCOPUS:84881151466

SN - 9780819495327

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Unmanned Systems Technology XV

T2 - Unmanned Systems Technology XV Conference

Y2 - 1 May 2013 through 3 May 2013

ER -

Real-time adaptive o-road vehicle navigation and terrain classification

Abstract

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Keywords

ASJC Scopus subject areas

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