Learning crop models for vision-based guidance of agricultural robots
Andrew English, Patrick Ross, David Ball, Ben Upcroft, Peter Corke
Abstract
This paper describes a vision-based method of guiding autonomous vehicles within crop rows in agricultural fields where the crop rows are challenging to detect or their appearance is not known a-priori. The location of the crop rows is estimated with an SVM regression algorithm using colour, texture and 3D structure descriptors from a forward facing stereo camera pair. Our system rapidly learns a model online with minimal user input, and then uses this model to track crop rows. Results demonstrate our method is able to learn and track a wide variety of crops with an RMS error of less than 3cm. We also present online control results demonstrating our system autonomously steering a robot for 3km.
BibTeX
@inproceedings{iros2015_learningcropmode,
title = {Learning crop models for vision-based guidance of agricultural robots},
author = {Andrew English and Patrick Ross and David Ball and Ben Upcroft and Peter Corke},
booktitle = {IROS 2015},
year = {2015}
}