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
More filters
Journal ArticleDOI
Towards robotic assistants in nursing homes: Challenges and results
TL;DR: A mobile robotic assistant, developed to assist elderly individuals with mild cognitive and physical impairments, as well as support nurses in their daily activities, is described, which demonstrated that it could autonomously provide reminders and guidance for elderly residents.
Proceedings ArticleDOI
An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements
TL;DR: In this paper a novel algorithm that combines Rao-Blackwellized particle filtering and scan matching is presented, which reduces the particle depletion problem that typically prevents the robot from closing large loops.
Proceedings Article
An Application of Markov Random Fields to Range Sensing
James Diebel,Sebastian Thrun +1 more
TL;DR: It is shown that by using an MRF to generate high-resolution, low-noise range images by integrating regular camera images into the range data, this technology can substantially improve over existing range imaging technology.
Proceedings ArticleDOI
Robust vehicle localization in urban environments using probabilistic maps
Jesse Levinson,Sebastian Thrun +1 more
TL;DR: This work proposes an extension to this approach to vehicle localization that yields substantial improvements over previous work in vehicle localization, including higher precision, the ability to learn and improve maps over time, and increased robustness to environment changes and dynamic obstacles.
Journal ArticleDOI
Lifelong Robot Learning
Sebastian Thrun,Tom M. Mitchell +1 more
TL;DR: It is argued that knowledge transfer is essential if robots are to learn control with moderate learning times in complex scenarios and two approaches which both capture invariant knowledge about the robot and its environments are presented.