首页|New Robotics and Automation Data Have Been Reported by Investigators at Shandong University (Rp-sg: Relation Prediction In 3d Scene Graphs for Unobserved Objects Localization)
New Robotics and Automation Data Have Been Reported by Investigators at Shandong University (Rp-sg: Relation Prediction In 3d Scene Graphs for Unobserved Objects Localization)
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NETL
NSTL
IEEE
Research findings on Robotics - Robotics and Automation are discussed in a new report. According to news reporting out of Weihai, People’s Republic of China, by NewsRx editors, research stated, “The ability to search for objects is a fundamental prerequisite for mobile robots when addressing a wide range of automation tasks. However, how to effectively estimate the positions of unobserved objects in a continuously changing environment remains an open challenge.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Shandong University, “Previous works have utilized probabilistic models to estimate the co-occurrence property between the target object and the observed landmark objects in a scene. However, few approaches can predict the precise spatial relations between objects based on a specific scene configuration. In this letter, we propose a novel unobserved object localization framework that achieves context-specific relation prediction based on the particular configuration of a scene. First, we leverage a 3D scene graph as a compact representation of the environment and propose a relation prediction model based on graph neural networks. This model can effectively interpret the information provided by the 3D scene graph and make accurate relation predictions. Second, to address the challenge of a high number of non-existent links between objects in the scene graph, we introduce a novel loss function that can better address imbalanced training data. Additionally, we propose an evaluation framework to comprehensively assess whether the relation prediction model benefits object search tasks.”
WeihaiPeople’s Republic of ChinaAsiaRobotics and AutomationRoboticsShandong University
NETL
NSTL
IEEE
