The Robotics Institute (RI) is a division of the School of Computer Science at Carnegie Mellon University in Pittsburgh, Pennsylvania, United States. A June 2014, the article in Robotics Business Review magazine calls it "the world's best robotics research facility" and a "pacesetter in robotics research and education."
Robotic mapping is a discipline related to computer vision and cartography. The goal for an autonomous robot is to be able to construct a map or floor plan and to localize itself and its recharging bases or beacons in it. Robotic mapping is that branch which deals with the study and application of ability to localize itself in a map / plan and sometimes to construct the map or floor plan by the autonomous robot.
Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve it in, at least approximately, tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality.
Sebastian Thrun is a German-American entrepreneur, educator, and computer scientist. He is CEO of Kitty Hawk Corporation, and chairman and co-founder of Udacity. Before that, he was a Google VP and Fellow, a Professor of Computer Science at Stanford University, and before that at Carnegie Mellon University. At Google, he founded Google X and Google's self-driving car team. He is also an adjunct professor at Stanford University and at Georgia Tech.
Monte Carlo localization (MCL), also known as particle filter localization, is an algorithm for robots to localize using a particle filter. Given a map of the environment, the algorithm estimates the position and orientation of a robot as it moves and senses the environment. The algorithm uses a particle filter to represent the distribution of likely states, with each particle representing a possible state, i.e., a hypothesis of where the robot is. The algorithm typically starts with a uniform random distribution of particles over the configuration space, meaning the robot has no information about where it is and assumes it is equally likely to be at any point in space. Whenever the robot moves, it shifts the particles to predict its new state after the movement. Whenever the robot senses something, the particles are resampled based on recursive Bayesian estimation, i.e., how well the actual sensed data correlate with the predicted state. Ultimately, the particles should converge towards the actual position of the robot.
Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots. Robotics is related to the sciences of electronics, engineering, mechanics, and software. The word "robot" was introduced to the public by Czech writer Karel Čapek in his play R.U.R., published in 1920. The term "robotics" was coined by Isaac Asimov in his 1941 science fiction short-story "Liar!"
Howie Choset is a professor at Carnegie Mellon University's Robotics Institute. His research includes snakebots, or robots designed in a segmented fashion to mimic snake-like actuation and motion, demining, and coverage. His snake robots have also been used in surgical applications for diagnosis and tumor removal; nuclear power plant inspection, archaeological excavations, manufacturing applications and understanding biological behaviors of a variety of animals.
Frank Dellaert is a professor in the School of Interactive Computing at the Georgia Institute of Technology. He is also affiliated with the IRIM@GT center and is well known for contributions to Robotics and Computer Vision.
Occupancy Grid Mapping refers to a family of computer algorithms in probabilistic robotics for mobile robots which address the problem of generating maps from noisy and uncertain sensor measurement data, with the assumption that the robot pose is known. Occupancy grids were first proposed by H. Moravec and A. Elfes in 1985.
Dieter Fox is a German-American roboticist and a Professor in the Department of Computer Science & Engineering at the University of Washington, Seattle. He received his PhD in Computer Science at the University of Bonn in 1998. He is most notable for his contributions to several fields including robotics, artificial intelligence, machine learning, and ubiquitous computing. Together with Wolfram Burgard and Sebastian Thrun he is a co-author of the book Probabilistic Robotics.
Seth A. Hutchinson is an American electrical and computer engineer. He is the Executive Director of the Institute for Robotics and Intelligent Machines at the Georgia Institute of Technology, where he is also Professor and KUKA Chair for Robotics in the School of Interactive Computing. His research in robotics spans the areas of planning, sensing, and control. He has published widely on these topics, and is coauthor of the books "Robot Modeling and Control," published by Wiley, Principles of Robot Motion - Theory, Algorithms, and Implementations, with Howie Choset, Kevin M. Lynch, George Kantor, Wolfram Burgard, Lydia E. Kavraki and Sebastian Thrun.
In robotics, the exploration problem deals with the use of a robot to maximize the knowledge over a particular area. The exploration problem arises in robotic mapping and search & rescue situations, where an environment might be dangerous or inaccessible to humans.
Jean-Claude Latombe is a French-American roboticist and the Kumagai Professor Emeritus in the School of Engineering at Stanford University. Latombe is a researcher in robot motion planning, and has authored one of the most highly cited books in the field.
Sven Koenig is a full professor in computer science at the University of Southern California. He received an M.S. degree in computer science from the University of California at Berkeley in 1991 and a Ph.D. in computer science from Carnegie Mellon University in 1997, advised by Reid Simmons.
Lydia E. Kavraki is a Greek-American computer scientist, the Noah Harding Professor of Computer Science, a professor of bioengineering, electrical and computer engineering, and mechanical engineering at Rice University. She is also the director of the Ken Kennedy Institute at Rice University. She is known for her work on robotics/AI and bioinformatics/computational biology and in particular for the probabilistic roadmap method for robot motion planning and biomolecular configuration analysis.
Armin Bernd Cremers is a German mathematician and computer scientist. He is a professor in the computer science institute at the University of Bonn, Germany. He is most notable for his contributions to several fields of discrete mathematics including formal languages and automata theory. In more recent years he has been recognized for his work in artificial intelligence, machine learning and robotics as well as in geoinformatics and deductive databases.
David Stavens is an American entrepreneur and scientist. He was co-founder and CEO of Udacity, a co-creator of Stanley, the winning car of the second driverless car competition of the DARPA Grand Challenge, and co-founder and CEO of Nines. Stavens has published in the fields of robotics, machine learning, and artificial intelligence.
Information engineering is the engineering discipline that deals with the generation, distribution, analysis, and use of information, data, and knowledge in systems. The field first became identifiable in the early 21st century.
Daniel Cremers is a German computer scientist, Professor of Informatics and Mathematics and Chair of Computer Vision & Artificial Intelligence at the Technische Universität München. His research foci are computer vision, mathematical image, partial differential equations, convex and combinatorial optimization, machine learning and statistical inference.
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