GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance

Khairuddin, Azhar; Talib, Mohamad Shukor; Haron, Habibollah; Abdullah, Muhamad Yazid Che

doi:10.1007/978-3-319-53480-0_6

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…MATLAB is used as the platform of this research, toolbox of FastSLAM which included the map of the environment is provided by Bailey [7]. It is selected because many works [2,3,8,9] have used the same environment map. In this map, uFA-FastSLAM by Musridho et al [4] is compared with original FastSLAM by Montemerlo et al [10] in terms of accuracy and convergence of robot and landmarks position estimation.…”

Section: Experimental Settingsmentioning

confidence: 99%

Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

Musridho

Hasan

Haron

et al. 2021

J. Phys.: Conf. Ser.

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In autonomous mobile robots, Simultaneous Localization and Mapping (SLAM) is a demanding and vital topic. One of two primary solutions of SLAM problem is FastSLAM. In terms of accuracy and convergence, FastSLAM is known to degenerate over time. Previous work has hybridized FastSLAM with a modified Firefly Algorithm (FA), called unranked Firefly Algorithm (uFA), to optimize the accuracy and convergence of the robot and landmarks position estimation. However, it has not shown the performance of the accuracy and convergence. Therefore, this work is done to present both mentioned performances of FastSLAM and uFA-FastSLAM to see which one is better. The result of the experiment shows that uFA-FastSLAM has successfully improved the accuracy (in other words, reduced estimation error) and the convergence consistency of FastSLAM. The proposed uFA-FastSLAM is superior compared to conventional FastSLAM in estimation of landmarks position and robot position with 3.30 percent and 7.83 percent in terms of accuracy model respectively. Furthermore, the proposed uFA-FastSLAM also exhibits better performances compared to FastSLAM in terms of convergence consistency by 93.49 percent and 94.20 percent for estimation of landmarks position and robot position respectively.

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Section: Experimental Settingsmentioning

confidence: 99%

Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

Musridho

Hasan

Haron

et al. 2021

J. Phys.: Conf. Ser.

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show abstract

“…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%

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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“…Tai-Zhi [14] proposed a new FastSLAM algorithm based on revised genetic resampling and a square-root unscented particle filter, in which a fast Metropolis-Hastings algorithm was used as the mutation operator and was combined with traditional crossover to form a new resampling method. Khairuddin [15] integrated the GA and PSO into FastSLAM, and overcame the particle depletion problem by improving the accuracy of FastSLAM in terms of robot and landmark set position estimation. However, in this approach, the scalar parameters of GA are undetermined, and need to be tuned empirically.…”

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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GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance

Cited by 4 publications

References 6 publications

Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

Robust Square-Root Cubature FastSLAM with Genetic Operators

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

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