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- W179362420 abstract "In traditional PF commonly used in electronic engineering, only the present information is used in a single interactive process and the information standing for the small weight of particles will be removed, leading to the problem that the information cannot be made full advantage of. This paper presents a joint SLAM algorithm combined Sparse Extended Information Filter (SEIF) and Particle Filter (PF) aiming to solve the problem occurring in the traditional particle filter algorithms. Information matrixes can keep all history information of poses related to a robot. With the application of Gibbs sampling, new particle set will be re-obtained resulting that posterior distribution can be described better and the state estimation precision is improved. A large amount of Monte-Carlo simulation experiments have proved that the algorithm proposed in this paper has improved the robot’s location precision by approximately 80% over the FastSLAM2.0." @default.
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- W179362420 date "2012-01-01" @default.
- W179362420 modified "2023-09-27" @default.
- W179362420 title "Investigation on SEIF Based on SLAM in Electronic Engineering" @default.
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- W179362420 doi "https://doi.org/10.1007/978-3-642-31516-9_95" @default.
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