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Follow Me: real-time in the wild person tracking application for autonomous robotics

  • In the last 20 years there have been major advances in autonomous robotics. In IoT (Industry 4.0), mobile robots require more intuitive interaction possibilities with humans in order to expand its field of applications. This paper describes a user-friendly setup, which enables a person to lead the robot in an unknown environment. The environment has to be perceived by means of sensory input. For realizing a cost and resource efficient Follow Me application we use a single monocular camera as low-cost sensor. For efficient scaling of our Simultaneous Localization and Mapping (SLAM) algorithm, we integrate an inertial measurement unit (IMU) sensor. With the camera input we detect and track a person. We propose combining state of the art deep learning with Convolutional Neural Network (CNN) and SLAM algorithms functionality on the same input camera image. Based on the output robot navigation is possible. This work presents the specification, workflow for an efficient development of the Follow Me application. Our application’s delivered point clouds are also used for surface construction. For demonstration, we use our platform SCITOS G5 equipped with the afore mentioned sensors. Preliminary tests show the system works robustly in the wild.

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Author of HS ReutlingenWeber, Thomas; Tryputen, Sergii; Danner, Michael; Rätsch, Matthias
Erschienen in:RoboCup 2017: Robot World Cup XXI. -(Lecture notes in artificial intelligence ; 11175)
Place of publication:Cham
Editor:Hidehisa Akiyama
Document Type:Conference proceeding
Publication year:2018
Tag:3D perception; CNN; Human-robot interaction; machine learning; mobile robotics; navigation; person tracking
Page Number:12
First Page:156
Last Page:167
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:006 Spezielle Computerverfahren
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt