@inproceedings{1d8f7de5e21440fd88fa9193251acefa,
title = "An Approach to 3D Object Detection in Real-Time for Cognitive Robotics Experiments",
abstract = "This paper presents a computer vision method that, taking information from an RGB-D camera, performs real time 3D object recognition to be used in cognitive robotics experiments, where the real time constraints are key. To this end, we have implemented and tested an algorithm that combines a deep neural network (YOLOv3 tiny) that processes RGB images and provides object recognition and 2D localization, with a point cloud analysis method to compute the third dimension. The proposed approach has been tested in real-time manipulation experiments with the UR5e robotic arm through ROS, and using a GPU to execute the method, showing that this combination allows for an efficient real-time learning using cognitive models.",
keywords = "3D object detection, Cognitive Robotics, Computer vision, Deep learning, Real-time processing, RGB-D camera",
author = "Daniel Vidal-Soroa and Pedro Furelos and Francisco Bellas and Becerra, {Jos{\'e} Antonio}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th Iberian Robotics Conference, ROBOT 2022 ; Conference date: 23-11-2022 Through 25-11-2022",
year = "2023",
doi = "10.1007/978-3-031-21065-5_24",
language = "English",
isbn = "9783031210648",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "283--294",
editor = "Danilo Tardioli and Vicente Matell{\'a}n and Guillermo Heredia and Silva, {Manuel F.} and Lino Marques",
booktitle = "ROBOT2022",
}