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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Visual simultaneous localisation and map-building supported by structured landmarks
R. Bączyk,Andrzej Kasiński +1 more
TL;DR: The method of using the operational map of robot surrounding to improve self-localisation accuracy of the robot camera and to reduce the size of the Kalman-filter state-vector with respect to the vector size involving point-wise environment features only is described.
Dissertation
A cognitive ego-vision system for interactive assistance
TL;DR: A visual active memory (VAM) is introduced as a flexible conceptual architecture for cognitive vision systems in general, and for assistance systems in particular, which adopts principles of human cognition to develop a representation for information stored in this memory.
Posted Content
Learning hierarchical behavior and motion planning for autonomous driving.
TL;DR: This work introduces hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution by integrating a classical sampling-based motion planner, of which the optimal cost is regarded as the rewards for high-level behavior learning.
Proceedings ArticleDOI
Pose estimation of unmanned ground vehicle based on dead-reckoning/GPS sensor fusion by unscented Kalman filter
TL;DR: This paper considers the problem of pose estimation of unmanned ground vehicle (UGV) equipped with a global positioning system, an odometer and an electronic compass, and proposes the method to calibrate the output of electronic compass.
Proceedings ArticleDOI
Segmentation-based online change detection for mobile robots
TL;DR: A novel algorithm for performing online change detection based on a previously developed robust online novelty detection system that uses a learned lower-dimensional representation of the feature space to perform measures of similarity and improves this change detection system by incorporating online scene segmentation to better utilize contextual information in the environment.
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