H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 111 Citations 58,948 245 World Ranking 79 National Ranking 48
Electronics and Electrical Engineering D-index 81 Citations 42,770 150 World Ranking 135 National Ranking 75

Research.com Recognitions

Awards & Achievements

2020 - ACM Fellow For contributions to probabilistic state estimation, RGB-D perception, and learning for robotics and computer vision

2015 - IEEE Fellow For contributions to Bayesian state estimation and robotic perception

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Dieter Fox spends much of his time researching Artificial intelligence, Robot, Computer vision, Mobile robot and Robotics. His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence. His study in Robot is interdisciplinary in nature, drawing from both Multimedia, Natural language and Human–computer interaction.

The Computer vision study combines topics in areas such as Range, Representation and Computer graphics. His Mobile robot research includes elements of Distributed computing and Markov chain. His research in the fields of Outline of robotics overlaps with other disciplines such as Field.

His most cited work include:

  • The dynamic window approach to collision avoidance (1879 citations)
  • Robust Monte Carlo localization for mobile robots (1552 citations)
  • Monte Carlo localization for mobile robots (1278 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Artificial intelligence, Robot, Computer vision, Mobile robot and Human–computer interaction. His Artificial intelligence research includes themes of Machine learning and Pattern recognition. His research in Robot focuses on subjects like Task, which are connected to Reinforcement learning.

The various areas that Dieter Fox examines in his Mobile robot study include Particle filter and Markov chain. The Particle filter study combines topics in areas such as Kalman filter and Algorithm. Dieter Fox interconnects Natural language and Plan in the investigation of issues within Human–computer interaction.

He most often published in these fields:

  • Artificial intelligence (67.63%)
  • Robot (48.79%)
  • Computer vision (35.27%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (67.63%)
  • Robot (48.79%)
  • Computer vision (35.27%)

In recent papers he was focusing on the following fields of study:

Dieter Fox focuses on Artificial intelligence, Robot, Computer vision, Object and Human–computer interaction. Artificial intelligence and Machine learning are commonly linked in his work. His Robot study integrates concerns from other disciplines, such as Task, Task analysis, Robustness and Trajectory.

The concepts of his Computer vision study are interwoven with issues in Code and Tactile sensor. His Object study combines topics in areas such as Feature, Manipulator, Usability, Particle filter and Synthetic data. His study focuses on the intersection of Human–computer interaction and fields such as Motion with connections in the field of Deep learning.

Between 2017 and 2021, his most popular works were:

  • PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes (511 citations)
  • The limits and potentials of deep learning for robotics (211 citations)
  • Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects. (197 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His main research concerns Artificial intelligence, Robot, Computer vision, Object and Artificial neural network. Dieter Fox regularly ties together related areas like Machine learning in his Artificial intelligence studies. His Robot study incorporates themes from Matching, Task, Task analysis, Trajectory and Robustness.

His work deals with themes such as Code and Tactile sensor, which intersect with Computer vision. His research integrates issues of Tracking, Feature and Synthetic data in his study of Object. In general Artificial neural network study, his work on Deep neural networks often relates to the realm of Fluid dynamics, Cohesion, Viscosity and Differentiable function, thereby connecting several areas of interest.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

The dynamic window approach to collision avoidance

D. Fox;W. Burgard;S. Thrun.
IEEE Robotics & Automation Magazine (1997)

2700 Citations

Robust Monte Carlo localization for mobile robots

Sebastian Thrun;Dieter Fox;Wolfram Burgard;Frank Dallaert.
Artificial Intelligence (2001)

2376 Citations

Monte Carlo localization for mobile robots

F. Dellaert;D. Fox;W. Burgard;S. Thrun.
international conference on robotics and automation (1999)

1964 Citations

Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)

Sebastian Thrun;Wolfram Burgard;Dieter Fox.
(2005)

1521 Citations

Monte Carlo localization: efficient position estimation for mobile robots

Dieter Fox;Wolfram Burgard;Frank Dellaert;Sebastian Thrun.
national conference on artificial intelligence (1999)

1487 Citations

A large-scale hierarchical multi-view RGB-D object dataset

Kevin Lai;Liefeng Bo;Xiaofeng Ren;Dieter Fox.
international conference on robotics and automation (2011)

1405 Citations

Markov localization for mobile robots in dynamic environments

Dieter Fox;Wolfram Burgard;Sebastian Thrun.
Journal of Artificial Intelligence Research (1999)

1305 Citations

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

Sebastian Thrun;Wolfram Burgard;Dieter Fox.
Machine Learning (1998)

1273 Citations

RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments

Peter Henry;Michael Krainin;Evan Herbst;Xiaofeng Ren.
The International Journal of Robotics Research (2012)

1238 Citations

Inferring activities from interactions with objects

M. Philipose;K.P. Fishkin;M. Perkowitz;D.J. Patterson.
IEEE Pervasive Computing (2004)

1120 Citations

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Best Scientists Citing Dieter Fox

Wolfram Burgard

Wolfram Burgard

University of Freiburg

Publications: 237

Sebastian Thrun

Sebastian Thrun

Stanford University

Publications: 133

Roland Siegwart

Roland Siegwart

ETH Zurich

Publications: 125

Gaurav S. Sukhatme

Gaurav S. Sukhatme

University of Southern California

Publications: 100

Michael Beetz

Michael Beetz

University of Bremen

Publications: 95

Takayuki Kanda

Takayuki Kanda

Kyoto University

Publications: 94

Hiroshi Ishiguro

Hiroshi Ishiguro

Osaka University

Publications: 94

Sergey Levine

Sergey Levine

University of California, Berkeley

Publications: 91

Norihiro Hagita

Norihiro Hagita

NTT (Japan)

Publications: 85

Manuela Veloso

Manuela Veloso

Carnegie Mellon University

Publications: 77

Sven Behnke

Sven Behnke

University of Bonn

Publications: 77

Cyrill Stachniss

Cyrill Stachniss

University of Bonn

Publications: 75

Patric Jensfelt

Patric Jensfelt

Royal Institute of Technology

Publications: 67

Nicholas Roy

Nicholas Roy

MIT

Publications: 66

Diane J. Cook

Diane J. Cook

Washington State University

Publications: 60

Gamini Dissanayake

Gamini Dissanayake

University of Technology Sydney

Publications: 59

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