Simultaneous Localization And Mapping Based on Variational Bayses Double-Scale Adaptive time-varying noise Cubature Kalman Filter

LI Shuaiyong; XIE Xianle; MAO Wenping; YANG Xuemei; NIE Jiawei

doi:10.11999/JEIT220031

Volume 45 Issue 3

Mar. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(3): 1006-1014

LI Shuaiyong, XIE Xianle, MAO Wenping, YANG Xuemei, NIE Jiawei. Simultaneous Localization And Mapping Based on Variational Bayses Double-Scale Adaptive time-varying noise Cubature Kalman Filter[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1006-1014. doi: 10.11999/JEIT220031

Citation:

LI Shuaiyong, XIE Xianle, MAO Wenping, YANG Xuemei, NIE Jiawei. Simultaneous Localization And Mapping Based on Variational Bayses Double-Scale Adaptive time-varying noise Cubature Kalman Filter[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1006-1014. doi: 10.11999/JEIT220031

Citation:

LI Shuaiyong, XIE Xianle, MAO Wenping, YANG Xuemei, NIE Jiawei. Simultaneous Localization And Mapping Based on Variational Bayses Double-Scale Adaptive time-varying noise Cubature Kalman Filter[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1006-1014. doi: 10.11999/JEIT220031

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Simultaneous Localization And Mapping Based on Variational Bayses Double-Scale Adaptive time-varying noise Cubature Kalman Filter

doi: 10.11999/JEIT220031

Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61703066), Chongqing Basic Research and Frontier Exploration Project (cstc2018jcyjAX0536), Chongqing Technology Innovation and Application Development Special Project (cstc2018jszx-cyztzxX0028, cstc2019jscx-fxydX0042, cstc2019jscx-zdztzxX0053)

Received Date: 2022-01-10
Rev Recd Date: 2022-06-22

Available Online: 2022-06-29

Publish Date: 2023-03-10

Abstract

Abstract

In order to solve the problem that the state estimation accuracy of mobile robot in Simultaneous Localization And Mapping (SLAM) is reduced due to the time-varying system noise and observed noise, a SLAM algorithm is proposed based on variational Bayes Double-Scale Adaptive time-varying noise Cubature Kalman Filter (DSACKF SLAM). The inverse Wishart distribution is used to model the one-step predicted error covariance matrix $ {{\boldsymbol{P}}_{k|k - 1}} $ and the observed noise covariance matrix ${{\boldsymbol{R}}_k}$ to reduce the influence of system noise and observed noise respectively, and the variational Bayes filter is used to estimate the mobile robot state matrix ${{\boldsymbol{X}}_k}$, $ {{\boldsymbol{P}}_{k|k - 1}} $ and ${{\boldsymbol{R}}_k}$. Simulation experiments are carried out under the time-varying and time-invariant conditions of system noise and observed noise respectively. The results show that, compared with the SLAM algorithm based on Unscented Kalman Filter (UKF SLAM) and the SLAM algorithm based on Variational Bayes Adaptive observed noise Cubature Kalman Filter (VB-ACKF SLAM), when the noise is time-varying, the average position error decreases by 1.54 m and 3.47 m respectively. When the noise is time-invariant, the average position error decreases by 0.62 m and 1.41 m respectively. The proposed DSACKF SLAM algorithm has better estimation performance.

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References(19)

References

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