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Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
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
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
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
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.