A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
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This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots as a constrained, probabilistic maximum-likelihood estimation problem, and devises a practical algorithm for generating the most likely map from data, along with the best path taken by the robot.Abstract:
This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approachread more
Citations
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Simultaneous localization and mapping: part I
TL;DR: This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method.
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A solution to the simultaneous localization and map building (SLAM) problem
TL;DR: The paper proves that a solution to the SLAM problem is indeed possible and discusses a number of key issues raised by the solution including suboptimal map-building algorithms and map management.
Dynamic bayesian networks: representation, inference and learning
Kevin Murphy,Stuart Russell +1 more
TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.
Proceedings ArticleDOI
FastSLAM: a factored solution to the simultaneous localization and mapping problem
TL;DR: FastSLAM as discussed by the authors is 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.
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Principles of Robot Motion: Theory, Algorithms, and Implementations
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TL;DR: In this paper, the mathematical underpinnings of robot motion are discussed and a text that makes the low-level details of implementation to high-level algorithmic concepts is presented.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book ChapterDOI
A New Approach to Linear Filtering and Prediction Problems
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
Journal ArticleDOI
An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.