User profiles for Sebastian Thrun
Sebastian ThrunStanford Verified email at stanford.edu Cited by 71323 |
[CITATION][C] Probabilistic robotics
Text classification from labeled and unlabeled documents using EM
Abstract This paper shows that the accuracy of learned text classifiers can be improved by
augmenting a small number of labeled training documents with a large pool of unlabeled
documents. This is important because in many text classification problems obtaining training
augmenting a small number of labeled training documents with a large pool of unlabeled
documents. This is important because in many text classification problems obtaining training
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[PDF][PDF] FastSLAM: A factored solution to the simultaneous localization and mapping problem
Abstract The ability to simultaneously localize a robot and accurately map its surroundings is
considered by many to be a key prerequisite of truly autonomous robots. However, few
approaches to this problem scale up to handle the very large number of landmarks present
considered by many to be a key prerequisite of truly autonomous robots. However, few
approaches to this problem scale up to handle the very large number of landmarks present
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[HTML][HTML] Robust Monte Carlo localization for mobile robots
Mobile robot localization is the problem of determining a robot's pose from sensor data. This
article presents a family of probabilistic localization algorithms known as Monte Carlo
Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted
article presents a family of probabilistic localization algorithms known as Monte Carlo
Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted
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Robotic mapping: A survey
S Thrun - Exploring artificial intelligence in the new millennium, 2002 - books.google.com
Abstract This article provides a comprehensive introduction into the field ot" robotic mapping,
with a focus on indoor mapping. It describes and compares various probabilistic techniques,
as they are presently being applied to a vast array of mobile robot mapping problems. The
with a focus on indoor mapping. It describes and compares various probabilistic techniques,
as they are presently being applied to a vast array of mobile robot mapping problems. The
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Monte carlo localization for mobile robots
Abstract To navigate reliably in indoor environments, a mobile robot must know where it is.
Thus, reliable position estimation is a key problem in mobile robotics. We believe that
probabilistic approaches are among the most promising candidates to providing a
Thus, reliable position estimation is a key problem in mobile robotics. We believe that
probabilistic approaches are among the most promising candidates to providing a
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[HTML][HTML] Learning metric-topological maps for indoor mobile robot navigation
S Thrun - Artificial Intelligence, 1998 - Elsevier
Autonomous robots must be able to learn and maintain models of their environments.
Research on mobile robot navigation has produced two major paradigms for mapping
indoor environments: grid-based and topological. While grid-based methods produce
Research on mobile robot navigation has produced two major paradigms for mapping
indoor environments: grid-based and topological. While grid-based methods produce
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[PDF][PDF] Monte carlo localization: Efficient position estimation for mobile robots
Abstract This paper presents a new algorithm for mobile robot localization, called Monte
Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic
approaches that have recently been applied with great practical success. However, previous
Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic
approaches that have recently been applied with great practical success. However, previous
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Stanley: The robot that won the DARPA Grand Challenge
S Thrun, M Montemerlo, H Dahlkamp… - Journal of field …, 2006 - Wiley Online Library
Abstract This article describes the robot Stanley, which won the 2005 DARPA Grand
Challenge. Stanley was developed for high-speed desert driving without manual
intervention. The robot's software system relied predominately on state-of-the-art artificial
Challenge. Stanley was developed for high-speed desert driving without manual
intervention. The robot's software system relied predominately on state-of-the-art artificial
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A probabilistic approach to concurrent mapping and localization for mobile robots
Abstract This paper addresses the problem of building large-scale geometric maps of indoor
environments with mobile robots. It poses the map building problem as a constrained,
probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for
environments with mobile robots. It poses the map building problem as a constrained,
probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for
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