2022 - Research.com Computer Science in Germany Leader Award
2015 - IEEE Fellow For contributions to mobile robot navigation and simultaneous localization and mapping
2014 - German National Academy of Sciences Leopoldina - Deutsche Akademie der Naturforscher Leopoldina – Nationale Akademie der Wissenschaften Informatics
2009 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to mobile robot navigation and environment modeling.
Wolfram Burgard mostly deals with Artificial intelligence, Mobile robot, Robot, Computer vision and Robotics. Wolfram Burgard has researched Artificial intelligence in several fields, including Algorithm and Machine learning. Wolfram Burgard interconnects Particle filter, Motion planning and Markov chain in the investigation of issues within Mobile robot.
His Robot research incorporates themes from Grid, Real-time computing, Position and Human–computer interaction. While the research belongs to areas of Computer vision, Wolfram Burgard spends his time largely on the problem of 3d model, intersecting his research to questions surrounding Surface. His study in the field of Outline of robotics also crosses realms of Field.
His primary areas of study are Artificial intelligence, Robot, Computer vision, Mobile robot and Robotics. Wolfram Burgard has included themes like Machine learning and Pattern recognition in his Artificial intelligence study. His Robot study incorporates themes from Task, Real-time computing, Simulation and Human–computer interaction.
His biological study spans a wide range of topics, including Grid, Odometry and Trajectory. His work deals with themes such as Particle filter, Motion planning and Algorithm, which intersect with Mobile robot. His Convolutional neural network research incorporates elements of Decoding methods and Deep learning.
Wolfram Burgard spends much of his time researching Artificial intelligence, Robot, Computer vision, Convolutional neural network and Human–computer interaction. His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition. His specific area of interest is Robot, where Wolfram Burgard studies Mobile robot.
His Computer vision study combines topics in areas such as Lidar, Visual odometry and Leverage. The Convolutional neural network study combines topics in areas such as Frame, Decoding methods and Image. His studies deal with areas such as Ground truth and Probabilistic logic as well as Object.
His primary areas of investigation include Artificial intelligence, Robot, Convolutional neural network, Computer vision and Robustness. His Robot research includes elements of Leverage, Task, Human–computer interaction, Object and Probabilistic logic. His Convolutional neural network research is multidisciplinary, relying on both Frame, Decoding methods, Image and Metric.
His study in Computer vision is interdisciplinary in nature, drawing from both Lidar and Mobile robot. His Mobile robot study combines topics from a wide range of disciplines, such as Street crossing, Generalization, Social force model and Multimodal interaction. Wolfram Burgard interconnects Image segmentation, Cost efficiency, Robot control, Simulation and Machine learning in the investigation of issues within Robustness.
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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)
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
G. Grisetti;C. Stachniss;W. Burgard.
IEEE Transactions on Robotics (2007)
Monte Carlo localization for mobile robots
F. Dellaert;D. Fox;W. Burgard;S. Thrun.
international conference on robotics and automation (1999)
G 2 o: A general framework for graph optimization
Rainer Kummerle;Giorgio Grisetti;Hauke Strasdat;Kurt Konolige.
international conference on robotics and automation (2011)
A benchmark for the evaluation of RGB-D SLAM systems
Jrgen Sturm;Nikolas Engelhard;Felix Endres;Wolfram Burgard.
intelligent robots and systems (2012)
OctoMap: an efficient probabilistic 3D mapping framework based on octrees
Armin Hornung;Kai M. Wurm;Maren Bennewitz;Cyrill Stachniss.
Autonomous Robots (2013)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Sebastian Thrun;Wolfram Burgard;Dieter Fox.
(2005)
Monte Carlo localization: efficient position estimation for mobile robots
Dieter Fox;Wolfram Burgard;Frank Dellaert;Sebastian Thrun.
national conference on artificial intelligence (1999)
Markov localization for mobile robots in dynamic environments
Dieter Fox;Wolfram Burgard;Sebastian Thrun.
Journal of Artificial Intelligence Research (1999)
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