[1] Zhai Hongqi, Wang Lihui, Cai Tijing, Meng Qian, et al. Robust SLAM localization methodbased on improved variational Bayesian filtering [J]. Journal of Southeast University (English Edition), 2022, 38 (4): 340-349. [doi:10.3969/j.issn.1003-7985.2022.04.003]

Copy

Robust SLAM localization methodbased on improved variational Bayesian filtering()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

38

Issue:

2022 4

Page:

340-349

Research Field:

Automation

Publishing date:

2022-12-20

Info

Title:

Robust SLAM localization methodbased on improved variational Bayesian filtering

Author(s):

Zhai Hongqi;  Wang Lihui;  Cai Tijing;  Meng Qian

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology of Ministry of Education, Southeast University, Nanjing 210096, China

Keywords:

underwater navigation and positioning;  non-Gaussian distribution;  time-varying noise;  variational Bayesian method;  simultaneous localization and mapping(SLAM)

PACS:

TP242.6

DOI:

10.3969/j.issn.1003-7985.2022.04.003

Abstract:

Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise, outliers, or unknown and time-varying noise statistical characteristics, a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is proposed. First, the measurement noise covariance is estimated using the variable Bayesian adaptive filtering algorithm. Then, the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted average. Finally, the system updates are iterated multiple times to further gradually correct the state estimation error. Furthermore, to observe features at different depths, a feature measurement model containing depth parameters is constructed. Experimental results show that when the measurement noise does not obey the Gaussian distribution and there are outliers in the measurement information, compared with the variational Bayesian adaptive SLAM method, the positioning accuracy of the proposed method is improved by 17.23%, 20.46%, and 17.76%, which has better applicability and robustness to environmental disturbance.

References:

[1] Connor J, Champion B, Joordens M A. Current algorithms, communication methods and designs for underwater swarm robotics: A review[J]. IEEE Sensors Journal, 2020, 21(1): 153-169. DOI:10.1109/JSEN.2020.3013265.
[2] Jalal F, Nasir F. Underwater navigation, localization and path planning for autonomous vehicles: A review[C]//International Bhurban Conference on Applied Sciences and Technologies. Islamabad, Pakistan, 2021: 817-828. DOI:10.1109/IBCAST51254.2021.9393315.
[3] Sahoo A, Dwivedy S K, Robi P S. Advancements in the field of autonomous underwater vehicle[J]. Ocean Engineering, 2019, 181: 145-160. DOI:10.1016/j.oceaneng.2019.04.011.
[4] González-García J, Gómez-Espinosa A, Cuan-Urquizo E, et al. Autonomous underwater vehicles: Localization, navigation, and communication for collaborative missions[J]. Applied Sciences, 2020, 10(4): 1256. DOI:10.3390/app10041256.
[5] Wu C, Wu Q, Ma F, et al. A novel positioning approach for an intelligent vessel based on an improved simultaneous localization and mapping algorithm and marine radar[J].Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 2019, 233(3): 779-792. DOI:10.1177/1475090218784449.
[6] Franchi M, Ridolfi A, Allotta B. Underwater navigation with 2D forward looking SONAR: An adaptive unscented Kalman filter-based strategy for AUVs[J]. Journal of Field Robotics, 2021, 38(3): 355-385. DOI:10.1002/rob.21991.
[7] Smith R, Self M, Cheeseman P. Estimating uncertain spatial relationships in robotics. Autonomous robot vehicles[M]. New York: Springer-Verlag, 1990: 167-193.
[8] Guivant J E, Nebot E M. Optimization of the simultaneous localization and map-building algorithm for real-time implementation[J]. IEEE Transactions on Robotics and Automation, 2001, 17(3): 242-257. DOI:10.1109/70.938382.
[9] Dissanayake M W M G, Newman P, Clark S, et al. A solution to the simultaneous localization and map building(SLAM)problem[J].IEEE Transactions on Robotics and Automation, 2001, 17(3): 229-241. DOI:10.1109/70.938381.
[10] Bailey T, Nieto J, Guivant J, et al. Consistency of the EKF-SLAM algorithm[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China, 2006: 3562-3568. DOI:10.1109/IROS.2006.281644.
[11] Carpenter R N. Concurrent mapping and localization with FLS[J].Proceedings of the 1998 Workshop on Autonomous Underwater Vehicles(Cat No 98CH36290). Cambridge, USA, 1998: 133-148. DOI:10.1109/AUV.1998.744449.
[12] Ribas D, Ridao P, Tardós J D, et al. Underwater SLAM in man-made structured environments[J]. Journal of Field Robotics, 2008, 25(11/12): 898-921. DOI:10.1002/rob.20249.
[13] Ribas D, Ridao P, Domingo Tardos J, et al. Underwater SLAM in a marina environment[C]//2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, CA, USA: 1455-1460. DOI:10.1109/IROS.2007.4399222.
[14] Zhang S J, He B, Ying L L, et al. Autonomous navigation with constrained consistency for C-ranger[J]. International Journal of Advanced Robotic Systems, 2014, 11(6): 84. DOI:10.5772/58582.
[15] Ye H, Zhou C. A new EKF SLAM algorithm of lidar-based AGV fused with bearing information[J]. TechConnect Briefs, 2018, 4: 32-39.
[16] Pei F J, Zhu M J, Wu X P. A decorrelated distributed EKF-SLAM system for the autonomous navigation of mobile robots[J].Journal of Intelligent & Robotic Systems, 2020, 98(3/4): 819-829. DOI:10.1007/s10846-019-01069-z.
[17] Zhang X, He B, Gao S, et al. Multiple model AUV navigation methodology with adaptivity and robustness[J].Ocean Engineering, 2022, 254: 111258. DOI:10.1016/j.oceaneng.2022.111258.
[18] Fraser C T, Ulrich S. Adaptive extended Kalman filtering strategies for spacecraft formation relative navigation[J].Acta Astronautica, 2021, 178: 700-721. DOI:10.1016/j.actaastro.2020.10.016.
[19] Huang H Q, Tang J C, Liu C, et al. Variational Bayesian-based filter for inaccurate input in underwater navigation[J].IEEE Transactions on Vehicular Technology, 2021, 70(9): 8441-8452. DOI:10.1109/TVT.2021.3099126.
[20] Tian Y Z, Suwoyo H, Wang W B, et al. An AEKF-SLAM algorithm with recursive noise statistic based on MLE and EM[J]. Journal of Intelligent & Robotic Systems, 2020, 97(2): 339-355. DOI:10.1007/s10846-019-01044-8.
[21] Sarkka S, Nummenmaa A. Recursive noise adaptive Kalman filtering by variational Bayesian approximations[J]. IEEE Transactions on Automatic Control, 2009, 54(3): 596-600. DOI:10.1109/TAC.2008.2008348.
[22] Chang G B, Chen C, Zhang Q Z, et al. Variational Bayesian adaptation of process noise covariance matrix in Kalman filtering[J].Journal of the Franklin Institute, 2021, 358(7): 3980-3993. DOI:10.1016/j.jfranklin.2021.02.037.
[23] Shan C H, Zhou W D, Yang Y F, et al. Multi-fading factor and updated monitoring strategy adaptive Kalman filter-based variational Bayesian[J].Sensors(Basel, Switzerland), 2020, 21(1): 198. DOI:10.3390/s21010198.
[24] Lin H S, Hu C. Variational inference based distributed noise adaptive Bayesian filter[J].Signal Processing, 2021, 178: 107775. DOI:10.1016/j.sigpro.2020.107775.
[25] Lin H S, Hu C. Variational inference based distributed noise adaptive Bayesian filter[J].Signal Processing, 2021, 178: 107775. DOI:10.1016/j.sigpro.2020.107775.
[26] Xu G, Huang Y L, Gao Z X, et al. A computationally efficient variational adaptive Kalman filter for transfer alignment[J].IEEE Sensors Journal, 2020, 20(22): 13682-13693. DOI:10.1109/JSEN.2020.3004621.
[27] Sun C J, Zhang Y G, Wang G Q, et al. A new variational Bayesian adaptive extended Kalman filter for cooperative navigation[J].Sensors(Basel, Switzerland), 2018, 18(8): 2538. DOI:10.3390/s18082538.
[28] Huber P J. Robust estimation of a location parameter[J]. Annals of Mathematical Statistics, 1964, 35(1):73-101.
[29] Chang L B, Hu B Q, Chang G B, et al. Robust derivative-free Kalman filter based on Huber’s M-estimation methodology[J].Journal of Process Control, 2013, 23(10): 1555-1561. DOI:10.1016/j.jprocont.2013.05.004.
[30] Chang L B, Hu B Q, Chang G B, et al. Multiple outliers suppression derivative-free filter based on unscented transformation[J]. Journal of Guidance, Control, and Dynamics, 2012, 35(6): 1902-1906. DOI:10.2514/1.57576.
[31] Chang G B, Liu M. M-estimator-based robust Kalman filter for systems with process modeling errors and rank deficient measurement models[J].Nonlinear Dynamics, 2015, 80(3): 1431-1449. DOI:10.1007/s11071-015-1953-0.
[32] Du H Y. Research for the application of simultaneous localization and mapping algorithm in autonomous underwater vehicle[D]. Harbin: Harbin Engineering University, 2012.(in Chinese)
[33] Ding H M. Research on the SLAM algorithm to the application of underwater vehicles based on EKF[D]. Harbin: Harbin Engineering University, 2014.(in Chinese)
[34] Yan G M, Weng J. Strapdown inertial navigation algorithm and integrated navigation principle[M]. Xi’an: Northwest Polytechnical University Press, 2019: 126-128.
[35] Ullah I, Su X, Zhang X W, et al. Simultaneous localization and mapping based on Kalman filter and extended Kalman filter[J]. Wireless Communications and Mobile Computing, 2020, 2020: 2138643. DOI:10.1155/2020/2138643.
[36] Ahmad H, Othman N A, Saari M M, et al. Data association analysis in simultaneous localization and mapping problem [J]. International Journal of Integrated Engineering, 2019, 11(4):112-118. DOI:10.30880/ijie.2019.11.04.012.
[37] Zhang T, Wu K Z, Song J W, et al. Convergence and consistency analysis for a 3-D invariant-EKF SLAM[J].IEEE Robotics and Automation Letters, 2017, 2(2): 733-740. DOI:10.1109/LRA.2017.2651376.

Memo

Memo:

Biographies: Zhai Hongqi(1990—), female, Ph.D. graduate; Wang Lihui(corresponding author), male, doctor, professor, wlhseu@163.com.
Foundation items: Primary Research and Development Plan of Jiangsu Province(No. BE2022389), Jiangsu Province Agricultural Science and Technology Independent Innovation Fund Project(No.CX(22)3091), the National Natural Science Foundation of China(No. 61773113).
Citation: Zhai Hongqi, Wang Lihui, Cai Tijing, et al. Robust SLAM localization method based on improved variational Bayesian filtering[J].Journal of Southeast University(English Edition), 2022, 38(4):340-349.DOI:10.3969/j.issn.1003-7985.2022.04.003.

Common functions