Bayesian Networks for Obstacle Classification in Agricultural Environments

Autor(es)

dos Santos, Edimilson Batista ; Teodoro Mendes, Caio Cesar ; Osorio, Fernando Santos ; Wolf, Denis Fernando ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2013 16TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS - (ITSC); v. N/A, p. 6-pg., 2013-01-01.

Resumo

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. (AU)

• Processo FAPESP: 11/21483-4 - Estimação de Navegabilidade para Veículos Autônomos Usando Aprendizado de Máquina.
• Beneficiário: Caio César Teodoro Mendes
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 08/57870-9 - Instituto de Sistemas Embarcados Críticos (ISEC)
• Beneficiário: Jose Carlos Maldonado
• Modalidade de apoio: Auxílio à Pesquisa - Temático