S
Sebastian Thrun
Researcher at Stanford University
Publications - 437
Citations - 108035
Sebastian Thrun is an academic researcher from Stanford University. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 146, co-authored 434 publications receiving 98124 citations. Previous affiliations of Sebastian Thrun include University of Pittsburgh & ETH Zurich.
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Concurrent mapping and localization for mobile robots with segmented local maps
TL;DR: A new probabilistic algorithm for the simultaneous localization and mapping problem is presented, based on matching of a set of local maps that have been obtained from range data that permits to eliminate unnecessary details, leading the method to be very appropriate for dynamical environments.
Sampling-Based Algorithms
Howie Choset,Kevin M. Lynch,Seth Hutchinson,George A. Kantor,Wolfram Burgard,Lydia E. Kavraki,Sebastian Thrun +6 more
TL;DR: This chapter contains sections titled: Probabilistic Roadmaps, Single-Query Sampling-Based Planners, Integration of Planners: Sampling Based Roadmap of Trees, Analysis of PRM, Beyond Basic Path Planning, and Problems.
Proceedings Article
A Case Study in Robotic Mapping of Abandoned Mines.
Christopher R. Baker,Zachary Omohundro,Scott M. Thayer,William Whittaker,Michael Montemerlo,Sebastian Thrun +5 more
Proceedings Article
Applying Metric-Trees to Belief-Point POMDPs
TL;DR: A new metric-tree algorithm which can be used in the context of POMDP planning to sort belief points spatially, and then perform fast value function updates over groups of points and results are presented showing that this approach can reduce computation in point-based POM DP algorithms for a wide range of problems.
Proceedings Article
Extracting Rules from Artifical Neural Networks with Distributed Representations.
TL;DR: An approach to the extraction of if-then rules from artificial neural networks using validity interval analysis, which is a generic tool for extracting symbolic knowledge by propagating rule-like knowledge through Backpropagation-style neural networks.