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Epipolar geometry on drones cameras for swarm robotics applications

Abstract:

Nowadays, Convolutional Neural Networks are commonly used in classification of images due to their accuracy and performance. In robotics, cameras are used as the main sensor to be able to gather information of the environment; however, image processing could require great amounts of resources. An approach for object detection for 3D environment navigation was implemented, using onboard 2D cameras of mini-drones. A TensorFlow network was fine-tuned for specific object classification and epipolar geometry used to obtain measures of distance between three drones and the detected items. An average measurement accuracy of 0.6094 was obtained, with an average projection error of the cameras of 0.08779. The processing time for object pbkp_rediction was approximately 0.02 seconds, which correspond to the 37% of the total time needed for a Node's iteration.