DAvinCi: A cloud computing framework for service robots

By Rajesh Vellore Arumugam, Vikas Reddy Enti, Bingbing Liu, Xiaojun Wu, Krishnamoorthy Baskaran, Foo Kong Foong, Appadorai Senthil Kumar, Dee Meng Kang, Wai Kit Goh. IEEE International Conference on Robotics and Automation, 2010

376 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/19db767cbca3b22f8569a91818cc089ff46c4c44

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In Aerospace Engineering

In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014, background): “Under a major European Union grant, the RoboEarch research team developed a series of system architectures for service robotics [30], [53], developing Cloud networking [74], [87], and computing resources [78] to generate 3D models of environments, speech recognition, and face recognition [148].”
Cloud robotics: architecture, challenges and applications (2012, methodology): “D models of environments, allowing robots to perform simultaneous localization and mapping (SLAM) much faster than by relying on their onboard computers [7].”
Cloud-based robot grasping with the google object recognition engine (2013, background): “1Department of Mechanical Engineering; benk@berkeley.edu 2Department of Electrical Engineering and Computer Science; amatsukawa@berkeley.edu 3Department of Industrial Engineering and Operations Research and Department of Electrical Engineering and Computer Science; goldberg@berkeley.edu 1–3 Universi”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “In [215], the DAvinCi architecture is presented.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “[135] and Rapyuta [136], also provide valuable lessons for developing edge-assisted SLAM for intelligent IoV.”
A Survey of Research on Cloud Robotics and Automation (2015, methodology): “Deep learning is a technique using many-layered neural networks that can take advantage of Big Data [47], and has been used for computer vision [94], [139] and grasping [101].”