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.
Papers
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Proceedings ArticleDOI
Self-supervised Monocular Road Detection in Desert Terrain
TL;DR: This method for identifying drivable surfaces in difficult unpaved and offroad terrain conditions as encountered in the DARPA Grand Challenge robot race achieves robustness by combining sensor information from a laser range finder, a pose estimation system and a color camera.
Book ChapterDOI
Particle Filters for Mobile Robot Localization
TL;DR: This chapter investigates the utility of particle filters in the context of mobile robotics, and reports results of applying particle filters to the problem of mobile robot localization, which is theproblem of estimating a robot’s pose relative to a map of its environment.
Issues in Using Function Approximation for Reinforcement Learning
Sebastian Thrun,Anton Schwartz +1 more
TL;DR: This paper gives a theoretical account of the phenomenon, deriving conditions under which one may expected it to cause learning to fail, and presents experimental results which support the theoretical findings.
Proceedings ArticleDOI
Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas
TL;DR: The Bayesian formula of the SLAM with DATMO problem is derived, which provides a solid basis for understanding and solving this problem, and a practical algorithm for performing DAT MO from a moving platform equipped with range sensors is provided.
Proceedings Article
Bayesian Network Induction via Local Neighborhoods
TL;DR: This work presents an efficient algorithm for learning Bayes networks from data by first identifying each node's Markov blankets, then connecting nodes in a maximally consistent way, and proves that under mild assumptions, the approach requires time polynomial in the size of the data and the number of nodes.