首页|Studies from Shenyang University of Technology Add New Findings in the Area of Robotics (Monocular Visual Navigation Algorithm for Nursing Robots Via Deep Learning Oriented To Dynamic Object Goal)
Studies from Shenyang University of Technology Add New Findings in the Area of Robotics (Monocular Visual Navigation Algorithm for Nursing Robots Via Deep Learning Oriented To Dynamic Object Goal)
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Springer Nature
Research findings on Robotics are discussed in a new report. According to news reporting out of Liaoning, People's Republic of China, by NewsRx editors, research stated, "Robot navigation systems suffer from relatively localizing the robots and object goals in the three-dimensional(3D) dynamic environment. Especially, most object detection algorithms adopt in navigation suffer from large resource consumption and a low calculation rate." Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Liaoning Provincial Department of Education Service Local Project, Ministry of Education Spring Program. Our news journalists obtained a quote from the research from the Shenyang University of Technology, "Hence, this paper proposes a lightweight PyTorch-based monocular vision 3D aware object goal navigation system for nursing robot, which relies on a novel pose-adaptive algorithm for inverse perspective mapping (IPM) to recover 3D information of an indoor scene from a monocular image. First, it detects objects and combines their location with the bird-eye view (BEV) information from the improved IPM to estimate the objects' orientation, distance, and dynamic collision risk. Additionally, the 3D aware object goal navigation network utilizes an improved spatial pyramid pooling strategy, which introduces an average-pooling branch and a max-pooling branch, better integrating local and global features and thus improving detection accuracy. Finally, a novel pose-adaptive algorithm for IPM is proposed, which introduces a novel voting mechanism to adaptively compensate for the monocular camera's pose variations to enhance further the depth information accuracy, called the adaptive IPM algorithm."
LiaoningPeople's Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningNano-robotRobotRoboticsShenyang University of Technology
NETL
NSTL
Springer Nature
