Terrain classification using static and dynamic texture features by uav downwash effect

• Autorzy: Carvalho João Pedro , Fonseca José Manuel , Mora André Damas

Identyfikatory

DOI

10.14313/JAMRIS_1-2019/11

• Języki publikacji: EN

Abstrakty

EN

Knowing how to identify terrain types is especially important in the autonomous navigation, mapping, decision making and emergency landings areas. For example, an unmanned aerial vehicle (UAV) can use it to find a suitable landing position or to cooperate with other robots to navigate across an unknown region. Previous works on terrain classification from RGB images taken onboard of UAVs shown that only static pixel-based features were tested with a considerable classification error. This paper presents a computer vision algorithm capable of identifying the terrain from RGB images with improved accuracy. The algorithm complement the static image features and dynamic texture patterns produced by UAVs rotors downwash effect (visible at lower altitudes) and machine learning methods to classify the underlying terrain. The system is validated using videos acquired onboard of a UAV with a RGB camera.

Słowa kluczowe

EN

image processing; texture; machine learning; terrain classificafion; neural networks; UAV

PL

przetwarzanie obrazu; tekstura; nauczanie maszynowe; sieci neuronowe

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2019
• Tom: Vol. 13, No. 1
• Strony: 84--93
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Carvalho João Pedro

• Computational Intelligence Group of CTS/UNINOVA, FCT, University NOVA of Lisbon

autor

Fonseca José Manuel

• Computational Intelligence Group of CTS/UNINOVA, FCT, University NOVA of Lisbon

autor

Mora André Damas

• Computational Intelligence Group of CTS/UNINOVA, FCT, University NOVA of Lisbon