Feature (linguistics)
Related papers: 20
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In robotics and AI, a **feature** refers to a measurable, distinctive property or descriptor extracted from raw sensor data — such as images, point clouds, or audio — that captures meaningful structure useful for perception and decision-making. Borrowed from linguistics, where features describe categorical properties of language units, the term in robotics broadly denotes any compact representation that encodes relevant information about the environment. Features may be geometric (e.g., edges, corners, surface normals), appearance-based (e.g., SIFT descriptors, color histograms), or learned (e.g., neural network activations). In practice, robots use features for tasks like simultaneous localization and mapping (SLAM), object detection, visual odometry, and gesture recognition — extracting stable, discriminative descriptors from sensor streams and matching them across views or time steps. Features matter because raw sensor data is high-dimensional and noisy; compact feature representations reduce computational cost, improve robustness to viewpoint and lighting changes, and enable reliable correspondence across observations. Whether hand-crafted or learned end-to-end, features form the perceptual backbone of nearly every robotic vision and navigation system.
Top Researchers
Top Institutes
Top Cited Papers
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Hugh Durrant‐Whyte, T. Bailey
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Computer and Robot Vision
Robert M. Haralock, Linda G. Shapiro
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MonoSLAM: Real-Time Single Camera SLAM
Andrew J. Davison, Ian Reid, Nicholas Molton, Olivier Stasse
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Yann LeCun, Koray Kavukcuoglu, Clément Farabet
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Real-time simultaneous localisation and mapping with a single camera
Davison
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Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, Ali Farhadi
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SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights
Michael Milford, Gordon Wyeth
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Fast 3D recognition and pose using the Viewpoint Feature Histogram
Radu Bogdan Rusu, Gary Bradski, R. Thibaux, JJ Hsu
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Dynamic sensor-based control of robots with visual feedback
L.E. Weiss, Arthur C. Sanderson, Charles P. Neuman
Citations: 822 • 1987
Socially aware motion planning with deep reinforcement learning
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Kalman filter-based algorithms for estimating depth from image sequences
Larry Matthies, Takeo Kanade, Richard Szeliski
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Visual SLAM algorithms: a survey from 2010 to 2016
Takafumi Taketomi, Hideaki Uchiyama, Sei Ikeda
Citations: 700 • 2017
Max-pooling convolutional neural networks for vision-based hand gesture recognition
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Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
Wei He, Yiting Dong
Citations: 638 • 2017
Model-based recognition in robot vision
R.T. Chin, Charles R. Dyer
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