Abstract
This paper presents solutions for methodological issues that can occur when obtaining face recognition and object detection for a ROS-based (Robot Operating System) open-source platform. Ubuntu 18.04, ROS Melodic and Google TensorFlow 1.14 are used in programming the software environment. TurtleBot2 (Kobuki) mobile robot with additional onboard sensors are used to conduct the experiments. Entire system configurations and specific hardware modifications that were proved mandatory to make out the system functionality are also clarified. Coding (e.g., Python) and sensors installations are detailed both in onboard and remote laptop computers. In experiments, TensorFlow face recognition and object detection are examined by using the TurtleBot2 robot. Results show how objects and faces were detected when the robot is navigating in the previously 2D mapped indoor environment.
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References
Kouzehgar M, Tamilselvam YK, Heredia MV, Elara MR (2019) Self-reconfigurable façade-cleaning robot equipped with deep-learning-based crack detection based on convolutional neural networks. Autom Constr 108
Tongloy T, Chuwongin S, Jaksukam K, Chousangsuntorn C, Boonsang S (2017) Asynchro-nous deep reinforcement learning for the mobile robot navigation with supervised auxiliary tasks. In: IEEE 2017 2nd international conference on robotics and automation engineering (ICRAE), pp 68–72
Millan-Romera JA, Perez-Leon H, Castillejo-Calle A, Maza I, Ollero A (2019) ROS-MAGNA, a ROS-based framework for the definition and management of multi-UAS coop-erative missions. In: IEEE 2019 international conference on unmanned aircraft systems (ICUAS), pp 1477–1486
Reddy PP (2019) Driverless car: software modelling and design using Python and Tensorflow, No. 1446, EasyChair
Gaifullin R, Ivanou M, Gazizov R (2019) Natural Human-Robot Interaction Toolkit. In: International conference on human interaction and emerging technologies. Springer, Cham, pp 196–200
ROS People Object Detection & Action Recognition Tensorflow, https://github.com/cagbal/ros_people_object_detection_tensorflow, last accessed 2020/10/30
Cervera E, Del Pobil AP (2019) Roslab: sharing ros code interactively with docker and ju-pyterlab. IEEE Robot Autom Mag 26(3):64–69
Make Your TurtleBot2 run on ROS Melodic (Ubuntu 18.04), https://github.com/gaunthan/Turtlebot2-On-Melodic, last accessed 2020/10/30
Akhmetzyanov A, Yagfarov R, Gafurov S, Ostanin M, Klimchik A (2019) Exploration of Underinvestigated Indoor Environment Based on Mobile Robot and Mixed Reality. In In-ternational Conference on Human Interaction and Emerging Technologies. Springer, Cham, pp 317–322
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Oksa, P., Salminen, T., Lipping, T. (2022). Obtaining a ROS-Based Face Recognition and Object Detection: Hardware and Software Issues. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_86
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DOI: https://doi.org/10.1007/978-981-16-2377-6_86
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