An improved FastSLAM 2.0 algorithm based on FC&amp;ASD-PSO

Lv, Taizhi; Feng, Maoyan

doi:10.1017/s0263574716000527

Cited by 7 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

Zhang

Chen³

2022

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

Simultaneous localization and mapping (SLAM) is a typical computing-intensive task. Based on its own computing power, a mobile robot has difficult meeting the real-time performance and accuracy requirements for the SLAM process at the same time. Benefiting from the rapid growth of the network data transmission rate, cloud computing technology begins to be applied in the robotics. There is the reliability problem caused by solely relying on cloud computing. To compensate for the insufficient airborne capacity, ensure the real-time performance and reliability, and improve the accuracy, a SLAM algorithm based on edge-cloud collaborative computing is proposed. The edge estimates the mobile robot pose and the local map using a square root unscented Kalman filter (SR-UKF). The cloud estimates the mobile robot pose and the global map using a distributed square root unscented particle filter (DSR-UPF). By using sufficient particles in the cloud, DSR-UPF can improve the SLAM accuracy. The cloud returns the particle with the largest posteriori probability to the edge, and the edge performs edge-cloud data fusion based on probability. Both the simulation and the experimental results show that the proposed algorithm can improve the estimation accuracy and reduce the execution time at the same time. By transferring the heavy computation from robots to the cloud, it can enhance the environmental adaptability of mobile robots.

show abstract

Section: Parallel Computingmentioning

confidence: 99%

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

Zhang

Chen³

2022

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

LSO-FastSLAM: A New Algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots

Zhu

Wang

et al. 2022

Sensors

View full text Add to dashboard Cite

This paper improves the accuracy of a mine robot’s positioning and mapping for rapid rescue. Specifically, we improved the FastSLAM algorithm inspired by the lion swarm optimization method. Through the division of labor between different individuals in the lion swarm optimization algorithm, the optimized particle set distribution after importance sampling in the FastSLAM algorithm is realized. The particles are distributed in a high likelihood area, thereby solving the problem of particle weight degradation. Meanwhile, the diversity of particles is increased since the foraging methods between individuals in the lion swarm algorithm are different so that improving the accuracy of the robot’s positioning and mapping. The experimental results confirmed the improvement of the algorithm and the accuracy of the robot.

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

View full text Add to dashboard Cite

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.

show abstract

An improved FastSLAM 2.0 algorithm based on FC&ASD-PSO

Cited by 7 publications

References 29 publications

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

LSO-FastSLAM: A New Algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots

Robust Square-Root Cubature FastSLAM with Genetic Operators

Contact Info

Product

Resources

About