Obtaining a ROS-Based Face Recognition and Object Detection: Hardware and Software Issues

Oksa, Petri; Salminen, Tero; Lipping, Tarmo (2022-09-24)
 
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Oksa, Petri
Salminen, Tero
Lipping, Tarmo
24.09.2022

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doi:10.1007/978-981-16-2377-6_86
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https://urn.fi/URN:NBN:fi:tuni-202203302882

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<p>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.</p>
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