Multiple camera fruit localization using a particle filter

Multiple camera fruit localization using a particle filter •Vision-only fruit localization method is presented that uses multiple inexpensive monocular cameras.•Fruit detection errors, due to inaccuracies in fruit segmentation, are included as a non-Gaussian stochastic noise in the sensor model.•Fruit motion (e.g., due to wind gust) is explicitly considered by developing a nonlinear fruit motion model and including that in the localization process.•The presented localization method is thoroughly analyzed by varying the design parameters, measurement noise, number of fruits, amount of overlap in clustered fruit scenarios, and fruit velocity. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers and Electronics in Agriculture Elsevier

Multiple camera fruit localization using a particle filter

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier B.V.
ISSN
0168-1699
eISSN
1872-7107
D.O.I.
10.1016/j.compag.2017.08.007
Publisher site
See Article on Publisher Site

Abstract

•Vision-only fruit localization method is presented that uses multiple inexpensive monocular cameras.•Fruit detection errors, due to inaccuracies in fruit segmentation, are included as a non-Gaussian stochastic noise in the sensor model.•Fruit motion (e.g., due to wind gust) is explicitly considered by developing a nonlinear fruit motion model and including that in the localization process.•The presented localization method is thoroughly analyzed by varying the design parameters, measurement noise, number of fruits, amount of overlap in clustered fruit scenarios, and fruit velocity.

Journal

Computers and Electronics in AgricultureElsevier

Published: Nov 1, 2017

References

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