• Publications
  • Influence
A large-scale hierarchical multi-view RGB-D object dataset
TLDR
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. Expand
The dynamic window approach to collision avoidance
TLDR
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. Expand
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
TLDR
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. Expand
Robust Monte Carlo localization for mobile robots
TLDR
A more robust algorithm is developed called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation of Monte Carlo Localization algorithms, and is applied to mobile robots equipped with range finders. Expand
Monte Carlo localization for mobile robots
TLDR
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. Expand
DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time
TLDR
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. Expand
Gaussian Processes for Data-Efficient Learning in Robotics and Control
TLDR
This paper learns a probabilistic, non-parametric Gaussian process transition model of the system and applies it to autonomous learning in real robot and control tasks, achieving an unprecedented speed of learning. Expand
Adapting the Sample Size in Particle Filters Through KLD-Sampling
  • D. Fox
  • Mathematics, Computer Science
  • Int. J. Robotics Res.
  • 1 December 2003
TLDR
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. Expand
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots
TLDR
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. Expand
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