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
LidarBoost: Depth superresolution for ToF 3D shape scanning
TL;DR: LidarBoost is presented, a 3D depth superresolution method that combines several low resolution noisy depth images of a static scene from slightly displaced viewpoints, and merges them into a high-resolution depth image.
Book Chapter
Simultaneous mapping and localization with sparse extended information filters
TL;DR: The notion of sparse extended information filters (SEIFs), as a new method for solving the SLAM problem, is developed, and several original constant-time results of SEIFs are presented, showing the high accuracy of the resulting maps in comparison to the computationally more cumbersome EKF solution.
Journal ArticleDOI
Real-time fault diagnosis [robot fault diagnosis]
TL;DR: In this paper, the authors present a number of complementary algorithms for detecting faults on-board operating robots, where a fault is defined as a deviation from expected behavior, and the algorithms focus on faults that cannot directly be detected from current sensor values but require inference from a sequence of timevarying sensor values.
Book ChapterDOI
The Mobile Robot Rhino
Joachim M. Buhmann,Wolfram Burgard,Armin B. Cremers,Dieter Fox,Thomas Hofmann,Frank E. Schneider,J. Strikos,Sebastian Thrun +7 more
TL;DR: The major components of the RHINO control software as they were exhibited at the AAAI Robot Competition and Exhibition are described and the basic philosophy of theRHINO architecture is sketched.
Proceedings ArticleDOI
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
TL;DR: This work proposes an algorithm that approximates POSGs as a series of smaller, related Bayesian games, using heuristics such as QMDP to provide the future discounted value of actions, and results in policies that are locally optimal with respect to the selected heuristic.