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- S. Thrun
- Computer ScienceCACM
This research presents a novel approach to planning and navigation algorithms that exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
FastSLAM: a factored solution to the simultaneous localization and mapping problem
This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map.
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.
Text Classification from Labeled and Unlabeled Documents using EM
- K. Nigam, A. McCallum, S. Thrun, Tom Michael Mitchell
- Computer ScienceMachine-mediated learning
- 1 May 2000
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and presents two extensions to the algorithm that improve classification accuracy under these conditions.
SCAPE: shape completion and animation of people
- Dragomir Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, James Davis
- Computer ScienceInternational Conference on Computer Graphics and…
- 1 July 2005
The SCAPE method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.
Learning to Track at 100 FPS with Deep Regression Networks
This work proposes a method for offline training of neural networks that can track novel objects at test-time at 100 fps, which is significantly faster than previous methods that use neural networks for tracking, which are typically very slow to run and not practical for real-time applications.
Point-based value iteration: An anytime algorithm for POMDPs
- Joelle Pineau, G. Gordon, S. Thrun
- Computer ScienceInternational Joint Conference on Artificial…
- 9 August 2003
This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning, and presents results on a robotic laser tag problem as well as three test domains from the literature.
In this paper we combine the Iterative Closest Point (’ICP’) and ‘point-to-plane ICP‘ algorithms into a single probabilistic framework. We then use this framework to model locally planar surface…
Dermatologist-level classification of skin cancer with deep neural networks
This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.