Frame (networking)
Related papers: 20
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A frame, in robotics and networking contexts, refers to a defined coordinate system or reference system used to describe the position and orientation of objects, sensors, or robots relative to one another in space. In robotics and AI, frames are fundamental building blocks for spatial reasoning: a robot may maintain separate frames for its base, end-effector, camera, and the world environment, with mathematical transformations linking them together. Algorithms such as SLAM (Simultaneous Localization and Mapping) continuously estimate and update these coordinate frame relationships as a robot navigates unknown environments, while hand-eye calibration determines the precise transformation between a robot's hand frame and an attached camera frame. Frames also appear in sensor fusion, object pose estimation, and multi-robot coordination, where consistent spatial representations across different reference points are essential. They matter because accurate frame management enables robots to interpret sensor data correctly, plan motions reliably, and interact safely with dynamic real-world environments — forming the geometric backbone upon which virtually all perception, planning, and control pipelines depend.
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Top Cited Papers
Parallel Tracking and Mapping for Small AR Workspaces
Georg Klein, David W. Murray
Citations: 4244 • 2007
A benchmark for the evaluation of RGB-D SLAM systems
Jrgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, Daniel Cremers
Citations: 3918 • 2012
On the Representation and Estimation of Spatial Uncertainty
Randall C. Smith, Peter Cheeseman
Citations: 1562 • 1986
Globally Consistent Range Scan Alignment for Environment Mapping
Feng Lu, Evangelos Milios
Citations: 1272 • 1997
A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery
Y. S. Kwoh, Jung-Fu Hou, Edmond Jonckheere, S. Hayati
Citations: 1257 • 1988
Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
Stefan Hinterstoißer, Vincent Lepetit, Slobodan Ilić, Stefan M. Holzer, Gary Bradski, Kurt Konolige, Nassir Navab
Citations: 1190 • 2013
DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes
Citations: 1152 • 2018
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
Berta Bescos, José M. Fácil, Javier Civera, José Neira
Citations: 924 • 2018
Dynamic sensor-based control of robots with visual feedback
L.E. Weiss, Arthur C. Sanderson, Charles P. Neuman
Citations: 822 • 1987
Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
Giuseppe Paolo, Ming Liu
Citations: 800 • 2017
Natural Actor-Critic
Jan Peters, Stefan Schaal
Citations: 751 • 2008
The Spatial Semantic Hierarchy
Benjamin Kuipers
Citations: 722 • 2000
Learning to Track: Online Multi-object Tracking by Decision Making
Xiang Yu, Alexandre Alahi, Silvio Savarese
Citations: 716 • 2015
BundleFusion
Angela Dai, Matthias Nießner, Michael Zollhöfer, Shahram Izadi, Christian Theobalt
Citations: 680 • 2017
Leader–follower formation control of nonholonomic mobile robots with input constraints
Luca Consolini, Fabio Morbidi, Domenico Prattichizzo, Mario Tosques
Citations: 671 • 2008
SemanticFusion: Dense 3D semantic mapping with convolutional neural networks
John McCormac, Ankur Handa, Andrew J. Davison, Stefan Leutenegger
Citations: 657 • 2017
Human Performance Issues and User Interface Design for Teleoperated Robots
Jessie Y. C. Chen, Ellen C. Haas
Citations: 633 • 2007
Design of HyQ – a hydraulically and electrically actuated quadruped robot
Claudio Semini, Nikos G. Tsagarakis, E. Guglielmino, Michele Focchi, Ferdinando Cannella, Darwin G. Caldwell
Citations: 627 • 2011
Dynamic 3-D shape measurement method: A review
Xianyu Su, Qican Zhang
Citations: 588 • 2009
University of Michigan North Campus long-term vision and lidar dataset
Nicholas Carlevaris‐Bianco, Arash K. Ushani, Ryan M. Eustice
Citations: 548 • 2015