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
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.
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.
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.
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.
The dynamic window approach to collision avoidance
D. Fox;W. Burgard;S. Thrun.
IEEE Robotics & Automation Magazine (1997)
Robust Monte Carlo localization for mobile robots
Sebastian Thrun;Dieter Fox;Wolfram Burgard;Frank Dallaert.
Artificial Intelligence (2001)
Monte Carlo localization for mobile robots
F. Dellaert;D. Fox;W. Burgard;S. Thrun.
international conference on robotics and automation (1999)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Sebastian Thrun;Wolfram Burgard;Dieter Fox.
Monte Carlo localization: efficient position estimation for mobile robots
Dieter Fox;Wolfram Burgard;Frank Dellaert;Sebastian Thrun.
national conference on artificial intelligence (1999)
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)
Markov localization for mobile robots in dynamic environments
Dieter Fox;Wolfram Burgard;Sebastian Thrun.
Journal of Artificial Intelligence Research (1999)
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Sebastian Thrun;Wolfram Burgard;Dieter Fox.
Machine Learning (1998)
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)
Inferring activities from interactions with objects
M. Philipose;K.P. Fishkin;M. Perkowitz;D.J. Patterson.
IEEE Pervasive Computing (2004)
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