A Mobile Robot Localization Method Based on Polar Scan Matching and Adaptive Niching Chaos Optimization Algorithm

Rim, Chol-Min; Sin, Yong-Chol; Paek, Kang-Hyok

doi:10.1007/s10846-022-01724-y

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…In order to evaluate the filtering accuracy of the unmanned-ship position information based on the proposed TCPF data fusion algorithm, first, the industrial computer equipped with the Linux system collected the position information of the unmanned ship using multiple sensors and outputted it from the serial port to the PC in the form of a log file through the wireless communication module. Then, the PC integrated the received position information of the unmanned ship into a data set [33]. After that, the designed TCPF data fusion algorithm was used to fuse and filter the position information data set of the unmanned ship, and finally realize the precise positioning of the unmanned ship.…”

Section: Establishment Of Unmanned-ship Experimental Environmentmentioning

confidence: 99%

Research on Multi-Sensor Data Fusion Positioning Method of Unmanned Ships Based on Threshold- and Hierarchical-Capacity Particle Filter

Shen,

Zhao,

Yuan

et al. 2023

Applied Sciences

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To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter (TCPF) is designed. First, the ZigBee-GPS/BDS multi-sensor measurement data is preprocessed to achieve a consistent space–time reference and transformed into the same coordinate system by projection. Then, the fault data is weighted and corrected through the consistency inspection of ZigBee-GPS/BDS multi-sensor positioning data, and the corresponding confidence factor is given according to the confidence distance of the positioning data; furthermore, the confidence factor is associated with stratified sampling. After that, the multi-sensor positioning data is filtered and denoised using a basic particle filter. Finally, a TCPF data fusion algorithm is designed, and the navigation positioning data of the unmanned ship is fused and filtered to obtain its positioning information. Numerical tests show that compared with other filtering algorithms, the mean square root error and standard deviation of the proposed TCPF algorithm decrease by an average of 25.0% and 28.0%, respectively, which verifies its high filtering accuracy and its advantages in suppressing particle degradation and avoiding sample scarcity. The experimental tests show that compared with other fusion algorithms, the proposed TCPF algorithm can not only realize the precise positioning during unmanned ship navigation, but also in the positioning and fault tolerance test, the average positioning error, root-mean-square error, and standard deviation of the former decrease by 36.0%, 38.0%, and 37.0%, respectively, and the corresponding performance indicators of the latter decrease by an average of 20.0%, 19.5%, and 17.5%, which verifies that it has the advantages of high data reliability and good filtering fault tolerance, and helps to improve the positioning accuracy of unmanned ships.

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Section: Establishment Of Unmanned-ship Experimental Environmentmentioning

confidence: 99%