T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots

The paper proposes landmark sequence data association for Simultaneous Localization and Mapping (SLAM) for data association problem under conditions of noise uncertainty increase. According to the space geometric information of the environment landmarks, the information correlations between the landmarks are constructed based on the graph theory. By observing the variations of the innovation covariance using the landmarks of the adjacent two steps, the problem is converted to solve the landmark TSP problem and the maximum correlation function of the landmark sequences, thus the data association of the observation landmarks is established. Finally, the experiments prove that our approach ensures the consistency of SLAM under conditions of noise uncertainty increase.

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Probabilistic Bases

2013

Simultaneous Localization and Mapping for Mobile Robots

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This is the third chapter of the first section. It is a compendium of all the concepts and theorems of probability theory that are found in the problems of Bayesian estimation of a robot location and the map of its environment. It presents uncertainty as an intrinsic feature of any mobile robot that develops in a real environment. It is then discussed how uncertainty has been treated along the history of science and how probabilistic approaches have represented such a huge success in many engineering fields, including robotics. The fundamental concepts of probability theory are discussed along with some advanced topics needed in further chapters, following a learning curve as smooth and comprehensive as possible.

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T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots

Cited by 3 publications

References 29 publications

Soft Computing Technique for Simultaneous Localization and Mapping of Mobile Robots

Soft Computing Technique for Simultaneous Localization and Mapping of Mobile Robots

Landmark Sequence Data Association for Simultaneous Localization and Mapping of Robots

Probabilistic Bases

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