Abstract
Vision-based navigation and obstacle detection must be sophisticated in order to perform well in complicated and diverse terrain, but that complexity comes at the expense of increased system latency between image capture and actuator signals. Increased latency, or a longer control loop, degrades the reactivity of the robot. We present a navigational framework that uses a self-supervised, learningbased obstacle detector without paying a price in latency and reactivity. A long-range obstacle detector uses online learning to accurately see paths and obstacles at ranges up to 30 meters, while a fast, short-range obstacle detector avoids obstacles at up to 5 meters. The learning-based long-range module is discussed in detail, and field experiments are described which demonstrate the success of the overall system.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics |
| Pages | 457-463 |
| Number of pages | 7 |
| State | Published - 2007 |
| Event | 13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics - Wurzburg, Germany Duration: Aug 29 2007 → Aug 31 2007 |
Other
| Other | 13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics |
|---|---|
| Country | Germany |
| City | Wurzburg |
| Period | 8/29/07 → 8/31/07 |
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Keywords
- LAGR
- Latency
- Learning
- Navigation
- Offroad
- Vision
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition
Cite this
A multi-range vision strategy for autonomous offroad navigation. / Hadsell, Raia; Erkan, Ayse; Sermanet, Pierre; Ben, Jan; Kavukcuoglu, Koray; Muller, Urs; LeCun, Yann.
Proceedings of the 13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics. 2007. p. 457-463.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A multi-range vision strategy for autonomous offroad navigation
AU - Hadsell, Raia
AU - Erkan, Ayse
AU - Sermanet, Pierre
AU - Ben, Jan
AU - Kavukcuoglu, Koray
AU - Muller, Urs
AU - LeCun, Yann
PY - 2007
Y1 - 2007
N2 - Vision-based navigation and obstacle detection must be sophisticated in order to perform well in complicated and diverse terrain, but that complexity comes at the expense of increased system latency between image capture and actuator signals. Increased latency, or a longer control loop, degrades the reactivity of the robot. We present a navigational framework that uses a self-supervised, learningbased obstacle detector without paying a price in latency and reactivity. A long-range obstacle detector uses online learning to accurately see paths and obstacles at ranges up to 30 meters, while a fast, short-range obstacle detector avoids obstacles at up to 5 meters. The learning-based long-range module is discussed in detail, and field experiments are described which demonstrate the success of the overall system.
AB - Vision-based navigation and obstacle detection must be sophisticated in order to perform well in complicated and diverse terrain, but that complexity comes at the expense of increased system latency between image capture and actuator signals. Increased latency, or a longer control loop, degrades the reactivity of the robot. We present a navigational framework that uses a self-supervised, learningbased obstacle detector without paying a price in latency and reactivity. A long-range obstacle detector uses online learning to accurately see paths and obstacles at ranges up to 30 meters, while a fast, short-range obstacle detector avoids obstacles at up to 5 meters. The learning-based long-range module is discussed in detail, and field experiments are described which demonstrate the success of the overall system.
KW - LAGR
KW - Latency
KW - Learning
KW - Navigation
KW - Offroad
KW - Vision
UR - http://www.scopus.com/inward/record.url?scp=56149097142&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=56149097142&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:56149097142
SN - 9780889866850
SP - 457
EP - 463
BT - Proceedings of the 13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics
ER -