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
TLDR
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.Abstract:
This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.read more
Citations
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Optical 3D laser measurement system for navigation of autonomous mobile robot
Luis C. Basaca-Preciado,Oleg Sergiyenko,Julio C. Rodriguez-Quinonez,Xochitl García,Vera Tyrsa,Moises Rivas-Lopez,Daniel Hernandez-Balbuena,Paolo Mercorelli,Mikhail A. Podrygalo,Alexander Gurko,Irina Tabakova,Oleg Starostenko +11 more
TL;DR: This paper proposes a robot navigation system which works using a high accuracy localization scheme by dynamic triangulation by integrating two principal systems, 3D laser scanning technical vision system (TVS) and mobile robot (MR) navigation system.
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Motion planning in urban environments: Ferguson, Howard, & Likhachev: Motion Planning in Urban Environments
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Leaving Flatland: Efficient real-time three-dimensional perception and motion planning
Radu Bogdan Rusu,Aravind Sundaresan,Benoit Morisset,Kris Hauser,Motilal Agrawal,Jean-Claude Latombe,Michael Beetz +6 more
TL;DR: The proposed system includes comprehensive localization, mapping, path planning, and visualization techniques for a mobile robot to operate autonomously in complex three-dimensional indoor and outdoor environments and is shown to be favorable for high-speed autonomous navigation.
Proceedings ArticleDOI
Feature-based terrain classification for LittleDog
Paul Filitchkin,Katie Byl +1 more
TL;DR: This work presents an approach that works with a single, compact camera and maintains high classification rates that are robust to changes in illumination and demonstrates that this approach is suitable for small legged robots by performing real-time terrain classification on LittleDog.
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Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras
TL;DR: It is concluded that the presented localization algorithm based on the probabilistic noise model of RSM features provides sufficient accuracy and reliability for autonomous driving system applications.
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