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Probabilistic robotics
TLDR
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. Expand
FastSLAM: a factored solution to the simultaneous localization and mapping problem
TLDR
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. Expand
Text Classification from Labeled and Unlabeled Documents using EM
TLDR
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. 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
Point-based value iteration: An anytime algorithm for POMDPs
TLDR
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. Expand
Learning to Track at 100 FPS with Deep Regression Networks
TLDR
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. Expand
Generalized-ICP
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 surfaceExpand
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
Robotic mapping: a survey
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 presentlyExpand
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