Matching (statistics)
Related papers: 20
About
Matching, in the context of statistics and robotics/AI, refers to the computational process of finding correspondences or alignments between two or more sets of data — such as sensor readings, images, point clouds, or feature descriptors. In robotics, matching is fundamental to tasks like simultaneous localization and mapping (SLAM), where laser range scans or visual features from successive observations must be aligned to build consistent environment maps. Techniques like Iterative Closest Point (ICP) align 3D point clouds for surface reconstruction and robot navigation, while template matching and feature-based methods enable object recognition and 6D pose estimation from camera imagery. Matching also underlies visual odometry, loop closure detection, and grasp synthesis, where candidate configurations are compared against known models or prior data. The importance of matching lies in its role as a bridge between raw sensory input and meaningful spatial or semantic understanding — without reliable correspondence estimation, robots cannot accurately localize themselves, recognize objects, or interact safely with their environment. Advances in deep learning have significantly improved matching robustness under challenging conditions like varying lighting, weather, and viewpoint changes.
Top Researchers
Top Institutes
Top Cited Papers
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Q. Al-Dujaili, Ye Duan, Omran Al-Shamma, José Santamaría, Mohammed A. Fadhel, Muthana Al‐Amidie, Laith Farhan
Citations: 7484 • 2021
Computer and Robot Vision
Robert M. Haralock, Linda G. Shapiro
Citations: 3952 • 1991
A survey of robot learning from demonstration
Brenna Argall, Sonia Chernova, Manuela Veloso, Brett Browning
Citations: 3252 • 2008
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, Ali Farhadi
Citations: 1507 • 2017
Globally Consistent Range Scan Alignment for Environment Mapping
Feng Lu, Evangelos Milios
Citations: 1272 • 1997
StereoScan: Dense 3d reconstruction in real-time
Andreas Geiger, Julius Ziegler, Christoph Stiller
Citations: 1102 • 2011
A flexible and scalable SLAM system with full 3D motion estimation
Stefan Kohlbrecher, Oskar von Stryk, Johannes Meyer, Uwe Klingauf
Citations: 1083 • 2011
SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights
Michael Milford, Gordon Wyeth
Citations: 970 • 2012
Visual simultaneous localization and mapping: a survey
Jorge Fuentes-Pacheco, J. Ruiz-Ascencio, Juan Manuel Rendón-Mancha
Citations: 886 • 2012
Data-Driven Grasp Synthesis—A Survey
Citations: 848 • 2014
The Representation, Recognition, and Locating of 3-D Objects
Olivier Faugeras, Martial Hebert
Citations: 796 • 1986
Matching robot appearance and behavior to tasks to improve human-robot cooperation
Jennifer L. Goetz, Sara Kiesler, Aaron Powers
Citations: 775 • 2004
Socially aware motion planning with deep reinforcement learning
Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How
Citations: 715 • 2017
Interactive Robots as Social Partners and Peer Tutors for Children: A Field Trial
Takayuki Kanda, Takayuki Hirano, Daniel Eaton, Hiroshi Ishiguro
Citations: 705 • 2004
A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping
Sebastian Thrun, Wolfram Burgard, D. Fox
Citations: 691 • 2002
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Feng Lu, Evangelos Milios
Citations: 672 • 1997
Mobile robot positioning: Sensors and techniques
J. Borenstein, H. R. Everett, Liang Feng, D.K. Wehe
Citations: 599 • 1997
Navigating Mobile Robots: Systems and Techniques
J. Borenstein, H. R. Everett, Liqiang Feng
Citations: 595 • 1996
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Yi Li, Gu Wang, Xiangyang Ji, Xiang Yu, Dieter Fox
Citations: 581 • 2018
An efficient fastslam algorithm for generating maps of large-scale cyclic environments from raw laser range measurements
Dirk Hähnel, Wolfram Burgard, D. Fox, Sebastian Thrun
Citations: 574 • 2004