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Titlebook: ROBOT2022: Fifth Iberian Robotics Conference; Advances in Robotics Danilo Tardioli,Vicente Matellán,Lino Marques Conference proceedings 202

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51#
發(fā)表于 2025-3-30 11:24:52 | 只看該作者
52#
發(fā)表于 2025-3-30 13:08:35 | 只看該作者
53#
發(fā)表于 2025-3-30 17:10:29 | 只看該作者
Conference proceedings 2023Spain, on November 23-25, 2022. ROBOT 2022 is part of a series of conferences that are a joint organization of SEIDROB—Sociedad Espa?ola para la Investigación y Desarrollo en Robótica/Spanish Society for Research and Development in Robotics, and SPR—Sociedade Portuguesa de Robótica/Portuguese Societ
54#
發(fā)表于 2025-3-30 23:28:17 | 只看該作者
Classification of Humans Social Relations Within Urban Areashere the best model achieves a very good performance, and for a real robot this classification is enough to be able to customize its behavior to its users. Furthermore, the proposed models show their future potential to improve its efficiency and to be implemented in a real robot.
55#
發(fā)表于 2025-3-31 03:34:09 | 只看該作者
Tree Trunks Cross-Platform Detection Using Deep Learning Strategies for?Forestry Operationse. The results showed that YOLOR was the best trunk detector achieving nearly 90% F1 score and an inference average time of 13.7?ms on GPU. This work will favour the development of advanced vision perception systems for robotics in forestry operations.
56#
發(fā)表于 2025-3-31 06:46:43 | 只看該作者
57#
發(fā)表于 2025-3-31 10:48:17 | 只看該作者
Limit Characterization for?Visual Place Recognition in?Underwater Scenests were conducted on two predefined trajectories containing appearance scene changes. The loop closure situations provided by the bag-of-words (BoW) approach are correctly detected, but it is sensitive to some severe conditions.
58#
發(fā)表于 2025-3-31 17:15:10 | 只看該作者
59#
發(fā)表于 2025-3-31 19:12:48 | 只看該作者
From Augmented Reality to?Deep Learning-Based Cognitive Assistance: An Overview for?Industrial Wire ly available datasets and techniques for training such semantic segmentation. To conclude, the fusion of Augmented Reality tools and semantic segmentation systems is postulated, to reduce both errors and cognitive overload of harnessing workers.
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