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- Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
- AAAI/IAAI
- 2002

The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the… (More)

- Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom M. Mitchell
- Machine Learning
- 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. This is important because in many text classification problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. We… (More)

- Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit
- IJCAI
- 2003

Proceedings of IJCAI 2003 In [15], Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM which overcomes important deficiencies of the original algorithm. We prove convergence of this new algorithm for linear SLAM… (More)

- Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun
- IJCAI
- 2003

This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points and then tracking the value and its derivative for those points only. By using stochastic trajectories to choose belief points, and by maintaining only one… (More)

- Dieter Fox, Wolfram Burgard, Sebastian Thrun
- IEEE Robot. Automat. Mag.
- 1997

This paper describes the dynamic window a p proach t o reactive collision avoidance for mobile robots equipped with synchro-drives. The a p proach i s d erived directly from the motion dynamics of the robot and i s t herefore particularly well-suited for robots o perating a t high speed. It diiers from previous approaches in that t he search for commands… (More)

- Sebastian Thrun, Dieter Fox, Wolfram Burgard, Frank Dellaert
- Artif. Intell.
- 2001

Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of… (More)

- Dragomir Anguelov, Praveen Srinivasan, Daphne Koller, Sebastian Thrun, Jim Rodgers, James Davis
- ACM Trans. Graph.
- 2005

We introduce the SCAPE method (Shape Completion and Animation for PEople)---a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations. We learn a <i>pose deformation model</i> that derives the non-rigid… (More)

- Frank Dellaert, Dieter Fox, Wolfram Burgard, Sebastian Thrun
- ICRA
- 1999

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that prob-abilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face… (More)

- James Diebel, Sebastian Thrun
- NIPS
- 2005

This paper describes a highly successful application of MRFs to the problem of generating high-resolution range images. A new generation of range sensors combines the capture of low-resolution range images with the acquisition of registered high-resolution camera images. The MRF in this paper exploits the fact that discontinuities in range and coloring tend… (More)

- Sebastian Thrun
- 2002

This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping. It describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems. The history of robotic mapping is also described, along with an extensive list of open research… (More)