Graf, FlorenzFlorenzGrafLindermayr, JochenJochenLindermayrGraf, BirgitBirgitGrafKraus, WernerWernerKrausHuber, Marco F.Marco F.Huber2024-07-162024-07-162024https://publica.fraunhofer.de/handle/publica/47138210.1109/TRO.2024.34207992-s2.0-85197651148Taking 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.enopen accessBiologically-Inspired RobotsHolistic Scene PerceptionLong-Term BenchmarkingSemantic Scene UnderstandingService robotsjournal article