Recognition and estimation of relative position of objects in controlled environments
Abstract
This project presents a system for recognition and classification of objects that are in its environment, for an assistance robot, as well as the estimation of their relative position with respect to the robot. For the recognition and classification of objects, we apply artificial vision techniques based on semantic segmentation tools, such as convolutional neural networks. For the estimation of the relative position of the objects, once identified, a stereoscopic vision technique was implemented. The results of the experiments show a 90.6% accuracy in recognition and classification, and an average error of 5 cm when estimating the relative position of the objects.
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References
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