Bayesian networks for obstacle classification in agricultural environments

Autonomous navigation in agricultural environments is a promising research topic for robotics, with several practical applications. This paper presents an obstacle detection system to operate in field scenarios that can accurately discern high and low vegetation from other types of obstacles. Our al...

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Bibliographic Details
Main Authors: dos Santos, Edimilson Batista, Mendes, Caio Cesar Teodoro, Osorio, Fernando Santos, Wolf, Denis Fernando
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Bayes methods
Cameras
Classification algorithms
Detection algorithms
Feature extraction
Filtering
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Summary:Autonomous navigation in agricultural environments is a promising research topic for robotics, with several practical applications. This paper presents an obstacle detection system to operate in field scenarios that can accurately discern high and low vegetation from other types of obstacles. Our algorithm is composed by three steps: (i) obstacle detection based on geometric information; (ii) clustering of detected obstacles; and (iii) filtering false positive detections using Bayesian classifiers. Several experimental tests have been carried out in citrus plantations. The results showed that our approach is able to correctly identify obstacles, classifying them as people, bushes, animals, and grass of different heights. In addition, the proposed approach could also be employed as a general framework for stereo-based obstacle detection.
ISSN:2153-0009
2153-0017
DOI:10.1109/ITSC.2013.6728429