Relational FastSLAM: an improved Rao-Blackwellized particle filtering framework using particle swarm characteristics

Lee, Seung Hwan; Eoh, Gyuho; Lee, Beom Hee

doi:10.1017/s0263574714002264

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Ye [20] proposed a random weight PSO strategy to improve the FastSLAM algorithm, to avoid particle degeneracy and maintain particle diversity. Seung [21] proposed a relational FastSLAM approach that integrated the PSO algorithm into the calculation of the weights and formation of the particles. Zuo [22] proposed a new FastSLAM method based on quantum-behaved PSO to improve the proposal distribution of particles and optimise the estimated particles.…”

Section: Introductionmentioning

confidence: 99%

A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot

et al. 2020

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The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an increasingly significant role in the SLAM problem. In order to enhance the performance of FastSLAM, a novel framework called IFastSLAM is proposed, based on particle swarm optimisation (PSO). In this framework, an adaptive resampling strategy is proposed that uses the genetic algorithm to increase the diversity of particles, and the principles of fractional differential theory and chaotic optimisation are combined into the algorithm to improve the conventional PSO approach. We observe that the fractional differential approach speeds up the iteration of the algorithm and chaotic optimisation prevents premature convergence. A new idea of a virtual particle is put forward as the global optimisation target for the improved PSO scheme. This approach is more accurate in terms of determining the optimisation target based on the geometric position of the particle, compared to an approach based on the maximum weight value of the particle. The proposed IFastSLAM method is compared with conventional FastSLAM, PSO-FastSLAM, and an adaptive generic FastSLAM algorithm (AGA-FastSLAM). The superiority of IFastSLAM is verified by simulations, experiments with a real-world dataset, and field experiments.

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Section: Introductionmentioning

confidence: 99%

A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot

et al. 2020

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“…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%

“…In Ref. [24], PSO is adopted to solve the impoverishment problems and particle depletion. In this paper, the particles optimize after resampling and directly maintain the sufficient number of particles which contributes to estimating the robot pose accurately.…”

Section: Introductionmentioning

confidence: 99%

Robust Square-Root Cubature FastSLAM with Genetic Operators

Havangi

2020

Robotica

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SUMMARY An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.

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A New Entrapment Based Invader Capture Strategy for Multi-robot Surveillance Systems

Lee

2022

Lecture Notes in Networks and Systems

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Relational FastSLAM: an improved Rao-Blackwellized particle filtering framework using particle swarm characteristics

Cited by 8 publications

References 13 publications

A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot

A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot

Robust Square-Root Cubature FastSLAM with Genetic Operators

A New Entrapment Based Invader Capture Strategy for Multi-robot Surveillance Systems

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