2016
DOI: 10.1017/s0263574716000527
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An improved FastSLAM 2.0 algorithm based on FC&ASD-PSO

Abstract: SUMMARYFastSLAM 2.0 is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem. The sampling process is one of the most important phases in the FastSLAM 2.0 framework. Its estimation accuracy depends heavily on a correct prior knowledge about the control and observation noise statistics (the covariance matrices Q and R). Without the correct prior knowledge about these matrices, the estimation accuracy of the robot path and landmark positions… Show more

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Cited by 7 publications
(3 citation statements)
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References 29 publications
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“…Incorrect a priori knowledge about the control and the observation noise matrices can seriously degrade the accuracy of these algorithms [27][28][29][30]. Based on the previous research [31], a dynamic fractional order and alpha stable distribution particle swarm optimization method is adopted, and the prior knowledge Q and R are adjusted dynamically by a fitness function. This fitness function is based on the inconsistency between the predicted observations and the observations.…”
Section: Parallel Computingmentioning
confidence: 99%
“…Incorrect a priori knowledge about the control and the observation noise matrices can seriously degrade the accuracy of these algorithms [27][28][29][30]. Based on the previous research [31], a dynamic fractional order and alpha stable distribution particle swarm optimization method is adopted, and the prior knowledge Q and R are adjusted dynamically by a fitness function. This fitness function is based on the inconsistency between the predicted observations and the observations.…”
Section: Parallel Computingmentioning
confidence: 99%
“…One of the problems is particle weight degradation and particle diversity loss. This problem is caused by the adoption of Rao-Blackwellized filter in the FastSLAM algorithm, which will decrease the accuracy of the FastSLAM algorithm [ 11 , 12 , 13 , 14 , 15 ]. Another problem of the FastSLAM algorithm is that it requires a large number of particles to maintain the accuracy of the algorithm in a complex environment, which will increase the running time of the algorithm and reduce the efficiency of the robot.…”
Section: Introductionmentioning
confidence: 99%
“…19,20 To overcome the above-mentioned limitations, there are many attempts that apply some biological evolution algorithms. Genetic algorithm 21 and particle swarm optimization (PSO) [22][23][24] are two commonly used methods to maintain the diversity of particles before resampling step. In Ref.…”
Section: Introductionmentioning
confidence: 99%