Autonomous Trail Following

Masoud Hoveidar-Sefid, Michael Jenkin

Abstract

Following off-road trails is somewhat more complex than following man-made roads. Trails are unstructured and typically lack standard markers that characterize roadways. Nevertheless, trails can provide an effective set of pathways for off-road navigation. Here we approach the problem of trail following by identifying trail-like regions; that is regions that are locally planar, contiguous with the robot’s current plane and which appear similar to the region in front of the robot. A multi-dimensional representation of the trail ahead is obtained by fusing information from an omnidirectional camera and a 3D LIDAR. A k-means clustering approach is taken based on this multi-dimensional signal to identify and follow off-road trails. This information is then used to compute appropriate steering commands for vehicle motion. Results are presented for over 1500 frames of video and laser scans of trails.

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Paper Citation


in Harvard Style

Hoveidar-Sefid M. and Jenkin M. (2017). Autonomous Trail Following . In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-264-6, pages 425-430. DOI: 10.5220/0006468404250430


in Bibtex Style

@conference{icinco17,
author={Masoud Hoveidar-Sefid and Michael Jenkin},
title={Autonomous Trail Following},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2017},
pages={425-430},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006468404250430},
isbn={978-989-758-264-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Autonomous Trail Following
SN - 978-989-758-264-6
AU - Hoveidar-Sefid M.
AU - Jenkin M.
PY - 2017
SP - 425
EP - 430
DO - 10.5220/0006468404250430