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Affiliation

Pattern Recognition Lab, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany

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1 Papers

RGB-D-based Human Detection and Segmentation for Mobile Robot Navigation in Industrial Environments

Topics: Segmentation and Grouping; Vision for Robotics; Mobile Imaging; Mobile and Egocentric Object Detection and Recognition

Authors: Oguz Kedilioglu ¹ , Markus Lieret ¹ , Julia Schottenhamml ² , Tobias Würfl ² , Andreas Blank ¹ , Andreas Maier ² and Jörg Franke ¹

Affiliations: ¹ Institute for Factory Automation and Production Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058 Erlangen, Germanyy , ² Pattern Recognition Lab, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany

Digital Object Identifier: 10.5220/0010187702190226

Proceedings of VISAPP 2021/VISIGRAPP, ISBN: 978-989-758-488-6

Quick Abstract

Automated guided vehicles (AGV) are nowadays a common option for the efficient and automated in-house transportation of various cargo and materials. By the additional application of unmanned aerial vehicles (UAV) in the delivery and intralogistics sector this flow of materials is expected to be extended by the third dimension within the next decade. To ensure a collision-free movement for those vehicles optical, ultrasonic or capacitive distance sensors are commonly employed. While such systems allow a collision-free navigation, they are not able to distinguish humans from static objects and therefore require the robot to move at a human-safe speed at any time. To overcome these limitations and allow an environment sensitive collision avoidance for UAVs and AGVs we provide a solution for the depth camera based real-time semantic segmentation of workers in industrial environments. The semantic segmentation is based on an adapted version of the deep convolutional neural network (CNN) architecture FuseNet. After explaining the underlying methodology we present an automated approach for the generation of weakly annotated training data and evaluate the performance of the trained model compared to other well-known approaches