WorldCat Identities

Fox, Dieter

Overview
Works: 28 works in 55 publications in 4 languages and 648 library holdings
Genres: Academic theses 
Roles: Other, Author
Classifications: TJ211, 629.892
Publication Timeline
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Most widely held works by Dieter Fox
Probabilistic robotics by Sebastian Thrun( Book )

10 editions published between 2005 and 2006 in English and held by 555 WorldCat member libraries worldwide

Probablistic robotics is a growing area in the subject, concerned with perception and control in the face of uncertainty and giving robots a level of robustness in real-world situations. This book introduces techniques and algorithms in the field
Personenschutz : Arbeitshandbuch by Klaus Stüllenberg( Book )

2 editions published between 1990 and 1997 in German and held by 21 WorldCat member libraries worldwide

Visual tracking of multiple humans with machine learning based robustness enhancement applied to real-world robotic systems by Suraj Nair( )

1 edition published in 2012 in English and held by 17 WorldCat member libraries worldwide

Probabilistic robotics by Sebastian Thrun( Book )

8 editions published between 2005 and 2010 in English and held by 15 WorldCat member libraries worldwide

Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, http://www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data
A Monte Carlo algorithm for multi-robot localization( Book )

3 editions published in 1999 in English and held by 4 WorldCat member libraries worldwide

Abstract: "This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots, using computer vision and laser range finding for detecting each other and estimating each other's relative location. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization."
A probabilistic approach for concurrent map acquisition and localization for mobile robots by Sebastian Thrun( Book )

3 editions published in 1997 in English and held by 4 WorldCat member libraries worldwide

Abstract: "This paper addresses the problem of building large- scale geometric maps if [sic] indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum- likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach."
Kakuritsu robotikusu( Book )

2 editions published in 2007 in Japanese and held by 3 WorldCat member libraries worldwide

The dynamic window approach to collision avoidance by Dieter Fox( Book )

1 edition published in 1995 in English and held by 3 WorldCat member libraries worldwide

Markov localization : a probabilistic framework for mobile robot localization and navigation by Dieter Fox( Book )

3 editions published in 1998 in English and German and held by 3 WorldCat member libraries worldwide

Coordinated Deployment of Multiple, Heterogeneous Robots( Book )

2 editions published in 2000 in English and held by 2 WorldCat member libraries worldwide

To be truly useful, mobile robots need to be fairly autonomous and easy to control. This is especially true in situations where multiple robots are used, due to the increase in sensory information and the fact that the robots can interfere with one another. This paper describes a system that integrates autonomous navigation, a task executive, task planning, and an intuitive graphical user interface to control multiple, heterogeneous robots. We have demonstrated a prototype system that plans and coordinates the deployment of teams of robots. Testing has shown the effectiveness and robustness of the system, and of the coordination strategies in particular
Experiences with an Interactive Museum Tour-Guide Robot by Wolfram Burgard( Book )

2 editions published in 1998 in English and held by 2 WorldCat member libraries worldwide

Abstract This article describes the software architecture of an autonomous, interactive tour-guide robot. It presents a modular, distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction. The approach does not require any modifications to the environment. To cope with the various challenges in dynamic and ill-structured environments, the software relies on probabilistic computation, on-line learning, any-time algorithms, and distributed control. Special emphasis has been placed on the design of interactive capabilities that appeal to people's intuition. In mid-1997, the robot was successfully deployed in a densely populated museum, demonstrating reliable operation in hazardous public environments, and raising the museum's attendance by more than 50%. In addition, people all over the world controlled the robot through the Web
Mixed-Initiative Control of Autonomous Unmanned Units Under Uncertainty( Book )

2 editions published in 2006 in English and held by 2 WorldCat member libraries worldwide

The MICA program focused on changing the control and coordination of unmanned aerial vehicles from a need for two to four persons per vehicle to one person controlling five or more vehicles. This program developed techniques for hierarchical control using mixed-initiative planning guidance and control taking a number of kinds of uncertainty into account at a fundamental level. These techniques focused on reasoning about uncertainty, including planning, belief tracking and communications with both human and automation. We developed this control model using Partially Observable Markov Decision Processes. The mixed-initiative interactions enabled users to describe constraints at multiple levels of the planning hierarchy. Techniques include visualization of the environment and optional speech input. The capabilities were demonstrated in a laboratory environment and on the program's Open Experimental Platform
Gai lü ji qi ren by Sebastian Thrun( Book )

1 edition published in 2017 in Chinese and held by 2 WorldCat member libraries worldwide

Ben shu dui gai lü ji qi ren xue zhei yi xin xing ling yu jin xing le quan mian de jie shao.Gai lü ji qi ren xue yi lai tong ji ji shu biao shi xin xi he jin xing jue ce,Rong na le dang jin da duo shu ji qi ren ying yong zhong bi ran cun zai de bu que ding xing,Shi ji qi ren xue de yi ge zhong yao fen bu.Bao kuo le ji chu zhi shi,Ding wei,Di tu gou jian,Gui hua yu kong zhi si da bu fen
The Interactive Museum Tour-Guide Robot( )

1 edition published in 1998 in Undetermined and held by 1 WorldCat member library worldwide

Toward never-ending object learning for robots by Yuyin Sun( )

1 edition published in 2016 in English and held by 1 WorldCat member library worldwide

A household robot usually works in a complex working environment, where it will continuously see new objects and encounter new concepts in its lifetime. Therefore, being able to learn more objects is crucial for the robot to be continuously useful over its lifespan. Moving beyond previous object learning research problem, of which mostly focuses on learning with given training objects and concepts, this research addresses the problem of enabling a robot to learn new objects and concepts continuously. Specifically, our contributions are as follows: First, we study how to accurately identify target objects in scenes based on human users' language descriptions. We propose a novel identification system using an object's visual attributes and names to recognize objects. We also propose a method to enable the system to recognize objects based on new names without seeing any training instances of the names. The attribute-based identification system improves both usability and accuracy over the previous ID-based object identification methods. Next, we consider the problem of organizing a large number of concepts into a semantic hierarchy. We propose a principle approach for creating semantic hierarchies of concepts via crowdsourcing. The approach can build hierarchies for various tasks and capture the uncertainty that naturally exists in these hierarchies. Experiments demonstrate that our method is more efficient, scalable, and accurate than previous methods. We also design a crowdsourcing evaluation to compare the hierarchies built by our method to expertly built ones. Results of the evaluation demonstrate that our approach outputs task-dependent hierarchies that can significantly improve user's performance of desired tasks. Finally, we build the first never-ending object learning framework, NEOL, that lets robots learn objects continuously. NEOL automatically learns to organize object names into a semantic hierarchy using the crowdsourcing method we propose. It then uses the hierarchy to improve the consistency and efficiency of annotating objects. Further, it adapts information from additional image datasets to learn object classifiers from a very small number of training examples. Experiments show that NEOL significantly improves robots' accuracy and efficiency in learning objects over previous methods
Integrating Topological and Metric Maps for Mobile Robot Navigation: A Statistical Approach( )

1 edition published in 1998 in Undetermined and held by 1 WorldCat member library worldwide

Position Estimation for Mobile Robots in Dynamic Environments( )

1 edition published in 1998 in Undetermined and held by 1 WorldCat member library worldwide

Extracting places and activities from GPS traces using hierarchical conditional random fields by Dieter Fox( )

in English and held by 1 WorldCat member library worldwide

Object recognition and semantic scene labeling for RGB-D data by Kevin Kar Wai Lai( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

The availability of RGB-D (Kinect-like) cameras has led to an explosive growth of research on robot perception. RGB-D cameras provide high resolution (640 x 480) synchronized videos of both color (RGB) and depth (D) at 30 frames per second. This dissertation demonstrates the thesis that combining of RGB and depth at high frame rates is helpful for various recognition tasks including object recognition, object detection, and semantic scene labeling. We present the RGB-D Object Dataset, a large dataset of 250,000 RGB-D images of 300 objects in 51 categories, and 22 RGB-D videos of objects in indoor home and office environments. We introduce algorithms for object recognition in RGB-D images that perform category, instance, and pose recognition in a scalable manner. We also present HMP3D, an unsupervised feature learning approach for 3D point cloud data, and demonstrate that HMP3D can be used to learn hierarchies of features from different attributes including color, gradient, shape, and surface normal orientation. Finally, we present a scene labeling approach for scenes constructed from RGB-D videos. The approach uses features learned from both individual RGB-D images and 3D point clouds constructed from entire video sequences. Through these applications, this thesis demonstrates the importance of designing new features and algorithms that specifically utilize the advantages of RGB-D cameras over traditional cameras and range sensors
Talking to robots : learning to ground human language in perception and execution by Cynthia Matuszek( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Advances in computation, sensing, and hardware are enabling robots to perform an increasing variety of tasks in progressively fewer constraints. It is now possible to imagine robots that can operate in traditionally human-centric environments. However, such robots need the flexibility to take instructions and learn about tasks from nonspecialists using language and other natural modalities. At the same time, physically grounded settings provide exciting opportunities for language learning. This thesis describes work on learning to acquire language for human-robot interaction in a physically grounded space. Two use cases are considered: learning to follow route directions through an indoor map, and learning about object attributes from people using unconstrained language and gesture. These problems are challenging because both language and real-world sensing tend to be noisy and ambiguous. This is addressed by reasoning and learning jointly about language and its physical context, parsing into intermediate formal representations that can be interpreted meaningfully by robotic systems. These systems can learn how to follow natural language directions through a map and how to identify objects from human descriptions, even when the underlying concepts are novel to the system, with success rates comparable to or defining the state of the art. Evaluations show that this work takes important steps towards building a robust, flexible, and effective mechanism for bringing together language acquisition and sensing to learn about the world
 
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Alternative Names
Dieter Fox deutscher Wissenschaftler

Dieter Fox Duits informaticus

Dieter Fox German roboticist

Dieter Fox professor académico alemão

Dieter Fox tysk datavetare

Dieter Fox tysk informatikar

Dieter Fox tysk informatiker

Fox, Dieter.

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