000 | 02212n a2200289#a 4500 | ||
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001 | 39556 | ||
003 | P5A | ||
005 | 20221213140630.0 | ||
007 | cr cuuuuuauuuu | ||
008 | 200122s20202020bl por d | ||
035 | _aocm51338542 | ||
040 |
_aP5A _cP5A |
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090 | _acs | ||
100 | 1 |
_aSoares, João Carlos Virgolino _u(PUC-Rio, Brazil) _9425 |
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245 | 1 | 0 |
_aVisual SLAM in Human Populated Environments: _bExploring the Trade-off between Accuracy and Speed of YOLO and Mask R-CNN/ _cJ. C. V. Soares. |
246 | 1 | 1 | _aSeminário de Computação Gráfica: |
260 |
_aRio de Janeiro: _bIMPA, _c2020. |
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300 | _avideo online | ||
505 | 2 | _aAbstract: Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics. However, the majority of Visual SLAM algorithms assume a static scenario, limiting their applicability in real-world environments. Dealing with dynamic content in Visual SLAM is still an open problem, with solutions usually relying on direct or feature-based methods. Deep learning techniques can improve the SLAM solution in environments with a priori dynamic objects, providing high-level information of the scene. This paper presents a new approach to SLAM in human populated environments using deep learning-based techniques. The system is built on ORB-SLAM2, a state-of-the-art SLAM system. The proposed methodology is evaluated using a benchmark dataset, outperforming other Visual SLAM methods in highly dynamic scenarios . | |
650 | 0 | 4 |
_aMatematica. _2larpcal _919899 |
697 |
_aCongressos e Seminários. _923755 |
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700 | 1 |
_aGattass, Marcelo _u(PUC-Rio, Brazil) _9426 |
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700 | 1 |
_aMeggiolaro, Marco _u(PUC-Rio, Brazil) _9427 |
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856 | 4 |
_zVIDEO _uhttps://www.youtube.com/watch?v=uKANJfw2ZP4&list=PLo4jXE-LdDTRkCsaH7C2rGXQg0wqKVYxp&index=2&t=0s |
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856 | 4 |
_zEVENTO _uhttp://seminarios.impa.br/visualizar/9128 |
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942 |
_2ddc _cBK |
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999 |
_aVISUAL SLAM in Human Populated Environments: Exploring the Trade-off between Accuracy and Speed of YOLO and Mask R-CNN. J. C. V. Soares. Rio de Janeiro: IMPA, 2020. video online. Disponível em: <https://www.youtube.com/watch?v=uKANJfw2ZP4&list=PLo4jXE-LdDTRkCsaH7C2rGXQg0wqKVYxp&index=2&t=0s>. Acesso em: 22 jan. 2020. _c38092 _d38092 |