M
Michael Montemerlo
Researcher at Google
Publications - 60
Citations - 17288
Michael Montemerlo is an academic researcher from Google. The author has contributed to research in topics: Simultaneous localization and mapping & Robot. The author has an hindex of 38, co-authored 60 publications receiving 16171 citations. Previous affiliations of Michael Montemerlo include Stanford University & Mitre Corporation.
Papers
More filters
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
TL;DR: This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map.
Journal ArticleDOI
Stanley: The Robot that Won the DARPA Grand Challenge
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara V. Nefian,Pamela Mahoney +29 more
TL;DR: The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.
Proceedings ArticleDOI
FastSLAM: a factored solution to the simultaneous localization and mapping problem
TL;DR: FastSLAM as discussed by the authors is an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map.
Proceedings Article
FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges
TL;DR: This paper describes a modified version of FastSLAM which overcomes important deficiencies of the original algorithm and proves convergence of this new algorithm for linear SLAM problems and provides real-world experimental results that illustrate an order of magnitude improvement in accuracy over the original Fast SLAM algorithm.
Journal IssueDOI
Junior: The Stanford entry in the Urban Challenge
Michael Montemerlo,Jan Becker,Suhrid Bhat,Hendrik Dahlkamp,Dmitri A. Dolgov,Scott M. Ettinger,Dirk Haehnel,Tim Hilden,Gabe Hoffmann,Burkhard Huhnke,Doug Johnston,Stefan Klumpp,Dirk Langer,Anthony Levandowski,Jesse Levinson,Julien Marcil,David Orenstein,Johannes Paefgen,Isaac Penny,Anna Petrovskaya,Mike Pflueger,Ganymed Stanek,David Stavens,Antone Vogt,Sebastian Thrun +24 more
TL;DR: The architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously, is presented, which successfully finished and won second place in the DARPA Urban Challenge, a robot competition organized by the U.S. Government.