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Open AccessJournal ArticleDOI

Stanley: The Robot that Won the DARPA Grand Challenge

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.

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Journal ArticleDOI

GeRoNa: Generic Robot Navigation

TL;DR: This paper presents the whole framework, detail every controller and provide an extensive experimental evaluation of the most important components of the GeRoNa (Generic Robot Navigation), a modular navigation framework for wheeled mobile robots.
Journal ArticleDOI

Characterizing a Heterogeneous System for Person Detection in Video Using Histograms of Oriented Gradients: Power Versus Speed Versus Accuracy

TL;DR: This paper presents a new implementation of the processing operations required in a widely-used pedestrian detection algorithm (the histogram of oriented gradients) when run in various configurations on a heterogeneous platform suitable for use as an embedded system and demonstrates that prioritization of each of these factors can be made by selecting a specific configuration.
Patent

Method and apparatus for routing ocean going vessels to avoid treacherous environments

TL;DR: In this paper, a computer implemented method, apparatus, and computer usable program code for generating a route for a vessel to travel from a start point to an end point is presented.
Proceedings ArticleDOI

Learning to assess terrain from human demonstration using an introspective Gaussian-process classifier

TL;DR: This paper presents an approach to learning robot terrain assessment from human demonstration that improves on current methods and uses a Gaussian-process classifier for terrain assessment due to its superior introspective abilities when compared to other classifier methods in the literature.
Proceedings ArticleDOI

Combining local trajectory planning and tracking control for autonomous ground vehicles navigating along a reference path

TL;DR: An integrated local trajectory planning and control scheme for the navigation of autonomous ground vehicles (AGVs) along a reference path with avoidance of static obstacles using an internal model controller to track the desired trajectory, while rejecting the negative effects resulting from model uncertainties and external disturbances.
References
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Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Proceedings ArticleDOI

New extension of the Kalman filter to nonlinear systems

TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
Book

Fundamentals of Vehicle Dynamics

TL;DR: In this article, the authors attempt to find a middle ground by balancing engineering principles and equations of use to every automotive engineer with practical explanations of the mechanics involved, so that those without a formal engineering degree can still comprehend and use most of the principles discussed.
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