| Autor: |
Graf, Florenz, Lindermayr, Jochen, Graf, Birgit, Kraus, Werner, Huber, Marco F. |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
2024 IEEE Transactions on Robotics (T-RO) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/TRO.2024.3420799 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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