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Your search keyword '"Appeldoorn, R.P.W."' showing total 2 results
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2 results on '"Appeldoorn, R.P.W."'

1. Tech United Eindhoven @Home 2019 champions paper

Author

van der Burgh, M.F.B., Lunenburg, J.J.M., Appeldoorn, R.P.W., van Beek, L.L.A.M., Geijsberts, J., Janssen, L.G.L., van Dooren, P., van Rooy, H.W.A.M., Aggarwal, A., Aleksandrov, S., Dang, K., Hofkamp, A. T., van Dinther, D., van de Molengraft, M.J.G., Chalup, Stephan, Niemueller, Tim, Suthakorn, Jackrit, Williams, Mary-Anne, Mechanical Engineering, Control Systems Technology, and Electromechanics and Power Electronics

Subjects
business.industry, Interface (Java), Computer science, Deep learning, media_common.quotation_subject, Object (computer science), Object detection, Human–computer interaction, Conversation, Artificial intelligence, State (computer science), business, Natural language, media_common
Abstract

This paper provides an overview of the main developments of the Tech United Eindhoven RoboCup @Home team. Tech United uses an advanced world modeling system called the Environment Descriptor. It allows straightforward implementation of localization, navigation, exploration, object detection & recognition, object manipulation and robot-robot cooperation skills based on the most recent state of the world. Other important features include object and people detection via deep learning methods, a GUI, speech recognition, natural language interpretation and a chat interface combined with a conversation engine. Recent developments that aided with obtaining the victory during RoboCup 2019 include pointing detection, usage of HSR’s display, a people detector and the addition of a custom keyboard in the chat interface.

Published
2019

2. Effective world modeling: multisensor data fusion methodology for automated driving

Author

Elfring, J., Appeldoorn, R.P.W., van den Dries, S., Kwakkernaat, M.R.J.A.E., Elfring, J., Appeldoorn, R.P.W., van den Dries, S., and Kwakkernaat, M.R.J.A.E.

Abstract

The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture

Published
2016

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