Off-road obstacle avoidance through end-to-end learning

Yann LeCun; Urs Muller; Jan Ben; Eric Cosatto; Beat Flepp

Off-road obstacle avoidance through end-to-end learning

Yann LeCun, Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp

Computer Science

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

Abstract

We describe a vision-based obstacle avoidance system for off-road mobile robots. The system is trained from end to end to map raw input images to steering angles. It is trained in supervised mode to predict the steering angles provided by a human driver during training runs collected in a wide variety of terrains, weather conditions, lighting conditions, and obstacle types. The robot is a 50cm off-road truck, with two forward-pointing wireless color cameras. A remote computer processes the video and controls the robot via radio. The learning system is a large 6-layer convolutional network whose input is a single left/right pair of unprocessed low-resolution images. The robot exhibits an excellent ability to detect obstacles and navigate around them in real time at speeds of 2 m/s.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Pages	739-746
Number of pages	8
State	Published - 2005
Event	2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada Duration: Dec 5 2005 → Dec 8 2005

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Other

Other	2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
Country/Territory	Canada
City	Vancouver, BC
Period	12/5/05 → 12/8/05

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

LeCun, Y, Muller, U, Ben, J, Cosatto, E & Flepp, B 2005, Off-road obstacle avoidance through end-to-end learning. in Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference. Advances in Neural Information Processing Systems, pp. 739-746, 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005, Vancouver, BC, Canada, 12/5/05.

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abstract = "We describe a vision-based obstacle avoidance system for off-road mobile robots. The system is trained from end to end to map raw input images to steering angles. It is trained in supervised mode to predict the steering angles provided by a human driver during training runs collected in a wide variety of terrains, weather conditions, lighting conditions, and obstacle types. The robot is a 50cm off-road truck, with two forward-pointing wireless color cameras. A remote computer processes the video and controls the robot via radio. The learning system is a large 6-layer convolutional network whose input is a single left/right pair of unprocessed low-resolution images. The robot exhibits an excellent ability to detect obstacles and navigate around them in real time at speeds of 2 m/s.",

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pages = "739--746",

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AU - Muller, Urs

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AU - Cosatto, Eric

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AB - We describe a vision-based obstacle avoidance system for off-road mobile robots. The system is trained from end to end to map raw input images to steering angles. It is trained in supervised mode to predict the steering angles provided by a human driver during training runs collected in a wide variety of terrains, weather conditions, lighting conditions, and obstacle types. The robot is a 50cm off-road truck, with two forward-pointing wireless color cameras. A remote computer processes the video and controls the robot via radio. The learning system is a large 6-layer convolutional network whose input is a single left/right pair of unprocessed low-resolution images. The robot exhibits an excellent ability to detect obstacles and navigate around them in real time at speeds of 2 m/s.

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Off-road obstacle avoidance through end-to-end learning

Abstract

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ASJC Scopus subject areas

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