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arXiv · 2404.17791

HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots

Florenz Graf·Jochen Lindermayr·Birgit Graf·Werner Kraus·Marco F. Huber
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Abstract

Taking over arbitrary tasks like humans do with a mobile service robot in open-world settings requires a holistic scene perception for decision-making and high-level control. This paper presents a human-inspired scene perception model to minimize the gap between human and robotic capabilities. The approach takes over fundamental neuroscience concepts, such as a triplet perception split into recognition, knowledge representation, and knowledge interpretation. A recognition system splits the background and foreground to integrate exchangeable image-based object detectors and SLAM, a multi-layer knowledge base represents scene information in a hierarchical structure and offers interfaces for high-level control, and knowledge interpretation methods deploy spatio-temporal scene analysis and perceptual learning for self-adjustment. A single-setting ablation study is used to evaluate the impact of each component on the overall performance for a fetch-and-carry scenario in two simulated and one real-world environment.

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Florenz Graf, Jochen Lindermayr, Birgit Graf, Werner Kraus, Marco F. Huber. 2024-04-27. HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots. https://doi.org/10.1109/tro.2024.3420799

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