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Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
- S. Thrun, W. Burgard, D. Fox
- Computer Science
- 1 September 2005
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
- Yu Xiang, Tanner Schmidt, V. Narayanan, D. Fox
- Computer ScienceRobotics: Science and Systems
- 1 November 2017
This work introduces PoseCNN, a new Convolutional Neural Network for 6D object pose estimation, which is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.
A large-scale hierarchical multi-view RGB-D object dataset
- Kevin Lai, Liefeng Bo, Xiaofeng Ren, D. Fox
- Computer ScienceIEEE International Conference on Robotics and…
- 9 May 2011
A large-scale, hierarchical multi-view object dataset collected using anRGB-D camera is introduced and techniques for RGB-D based object recognition and detection are introduced, demonstrating that combining color and depth information substantially improves quality of results.
The dynamic window approach to collision avoidance
- D. Fox, W. Burgard, S. Thrun
- Computer ScienceIEEE Robotics Autom. Mag.
- 1 March 1997
This approach, designed for mobile robots equipped with synchro-drives, is derived directly from the motion dynamics of the robot and safely controlled the mobile robot RHINO in populated and dynamic environments.
Robust Monte Carlo localization for mobile robots
- S. Thrun, D. Fox, W. Burgard, F. Dellaert
- Computer ScienceArtificial Intelligence
- 1 May 2001
Monte Carlo localization for mobile robots
- F. Dellaert, D. Fox, W. Burgard, S. Thrun
- Computer ScienceProceedings IEEE International Conference on…
- 10 May 1999
The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time
- Richard A. Newcombe, D. Fox, S. Seitz
- Materials ScienceComputer Vision and Pattern Recognition
- 7 June 2015
This work presents the first dense SLAM system capable of reconstructing non-rigidly deforming scenes in real-time, by fusing together RGBD scans captured from commodity sensors, and displays the updated model in real time.
Adapting the Sample Size in Particle Filters Through KLD-Sampling
- D. Fox
- Computer ScienceInt. J. Robotics Res.
- 1 December 2003
A statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process by bounding the approximation error introduced by the sample-based representation of the particle filter.
DeepIM: Deep Iterative Matching for 6D Pose Estimation
- Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, D. Fox
- Computer ScienceInternational Journal of Computer Vision
- 31 March 2018
A novel deep neural network for 6D pose matching named DeepIM is proposed, trained to predict a relative pose transformation using a disentangled representation of 3D location and 3D orientation and an iterative training process.
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots
- D. Fox, W. Burgard, F. Dellaert, S. Thrun
- Computer ScienceAAAI/IAAI
- 18 July 1999
Monte Carlo Localization is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success and yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches.
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