Mobile robot monocular vision navigation based on road region and boundary estimation
Abstract
We present a monocular vision-based navigation system that incorporates two contrasting approaches: region segmentation that computes the road appearance, and road boundary detection that estimates the road shape. The former approach segments the image into multiple regions, then selects and tracks the most likely road appearance. On the other hand, the latter detects the vanishing point and road boundaries to estimate the shape of the road. Our algorithm operates in urban road settings and requires no training or camera calibration to maximize its adaptability to many environments. We tested our system in 1 indoor and 3 outdoor urban environments using our ground-based robot, Beobot 2.0, for real-time autonomous visual navigation. In 20 trial runs the robot was able to travel autonomously for 98.19% of the total route length of 316.60 m.
Additional Information
© 2012 IEEE. The authors gratefully acknowledge the support of NSF, General Motors, ARO and DARPA. The authors affirm that the views expressed herein are solely their own, and do not represent the views of the United States government or any agency thereof.Additional details
- Eprint ID
- 73959
- DOI
- 10.1109/IROS.2012.6385703
- Resolver ID
- CaltechAUTHORS:20170201-152438320
- NSF
- General Motors
- Army Research Office (ARO)
- Defense Advanced Research Projects Agency (DARPA)
- Created
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2017-02-02Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field