Journal of Shanghai Jiao Tong University (Science) ›› 2020, Vol. 25 ›› Issue (1): 76-87.doi: 10.1007/s12204-019-2146-7
Previous Articles Next Articles
ZHANG Zhuqing1 (张铸青), DONG Peng 1* (董鹏), TUO Hongya1 (庹红娅), LIU Guangjun2 (刘光军), JIA He1 (贾鹤)
Online:
2020-01-15
Published:
2020-01-12
Contact:
DONG Peng (董鹏)
E-mail:ongpengkty@sjtu.edu.cn
CLC Number:
ZHANG Zhuqing (张铸青), DONG Peng (董鹏), TUO Hongya (庹红娅), LIU Guangjun (刘光军), JIA He (贾鹤). Robust Variational Bayesian Adaptive Cubature Kalman Filtering Algorithm for Simultaneous Localization and Mapping with Heavy-Tailed Noise[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(1): 76-87.
[1] | DURRANT-WHYTE H, BAILEY T. Simultaneous localization and mapping: Part I [J]. IEEE Robotics & Automation Magazine, 2006, 13(2): 99-107. |
[2] | BAILEY T, DURRANT-WHYTE H. Simultaneous localization and mapping (SLAM): Part II [J]. IEEE Robotics & Automation Magazine, 2006, 13(3): 108-117. |
[3] | CADENA C, CARLONE L, CARRILLO H, et al.Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age [J].IEEE Transactions on Robotics, 2016, 32(6): 1309-1332. |
[4] | MOURIKIS A I, ROUMELIOTIS S I. A multistate constraint Kalman ˉlter for vision-aided inertial navigation [C]//IEEE International Conference on Robotics and Automation. Rome, Italy: IEEE, 2007:3565-3572. |
[5] | BLOESCHM, BURRI M, OMARI S, et al. Iterated extended Kalman ˉlter based visual-inertial odometry using direct photometric feedback [J]. The International Journal of Robotics Research, 2017, 36(10):1053-1072. |
[6] | SHOJAIE K, SHAHRI A M. Iterated unscented SLAM algorithm for navigation of an autonomous mobile robot [C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, France: IEEE,2008: 1582-1587. |
[7] | WANG H J, FU G X, LI J, et al. An adaptive UKF based SLAM method for unmanned underwater vehicle [J]. Mathematical Problems in Engineering, 2013,2013: 605981. |
[8] | BLOESCH M, OMARI S, HUTTER M, et al. Robust visual inertial odometry using a direct EKF-based approach [C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg, Germany:IEEE, 2015: 298-304. |
[9] | XIA J Z, IQBAL U, NOURELDIN A, et al. Adaptive square-root CKF based SLAM algorithm for indoor UGVs [C]//IEEE International Conference on Mechatronics and Automation. Takamatsu, Japan: IEEE,2017: 1942-1946. |
[10] | CHANDRA K P B, GU D W, POSTLETHWAITE I.Cubature Kalman ˉlter based localization and mapping [J]. IFAC Proceedings Volumes, 2011, 44(1):2121-2125. |
[11] | ENGEL J, KOLTUN V, CREMERS D. Direct sparse odometry [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 611-625. |
[12] | MUR-ARTAL R, MONTIEL J M M, TARD?OS J D. ORB-SLAM: A versatile and accurate monocular SLAM system [J]. IEEE Transactions on Robotics,2015, 31(5): 1147-1163. |
[13] | FORSTER C, PIZZOLI M, SCARAMUZZA D.SVO: Fast semi-direct monocular visual odometry[C]//IEEE International Conference on Robotics and Automation. Hong Kong, China: IEEE, 2014: 15-22. |
[14] | SMITH R, SELF M, CHEESEMAN P. Estimating uncertain spatial relationships in robotics [C]//IEEE International Conference on Robotics and Automation.Raleigh, NC, USA: IEEE, 1987: 850-850. |
[15] | WILLIAMS B, CUMMINS M, NEIRA J, et al. A comparison of loop closing techniques in monocular SLAM[J]. Robotics and Autonomous Systems, 2009, 57(12):1188-1197. |
[16] | MONTERNERLO M, THRUN S, KOLLER D, et al.FastSLAM: A factored solution to the simultaneous localization and mapping problem [C]//The Eighteenth National Conference on Artiˉcial Intelligence and the Fourteenth Annual Conference on Innovative Applications of Artiˉcial Intelligence. Menlo Park, California,USA: AAAI, 2002: 593-598. |
[17] | JULIER S, UHLMANN J, DURRANT-WHYTE H F. A new method for the nonlinear transformation of means and covariances in ˉlters and estimators[J]. IEEE Transactions on Automatic Control, 2000,45(3): 477-482. |
[18] | ARASARATNAM I, HAYKIN S. Cubature Kalman filters [J]. IEEE Transactions on Automatic Control,2009, 54(6): 1254-1269. |
[19] | GIL A, REINOSO ? O, MOZOS ?O M, et al. Improving data association in vision-based SLAM[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China: IEEE, 2006:2076-2081. |
[20] | NAKABAYASHI A, UENO G. An extension of the ensemble Kalman ˉlter for estimating the observation error covariance matrix based on the variational Bayes'smethod [J]. Monthly Weather Review, 2017, 145(1):199-213. |
[21] | JANCZAK D. Estimation method for measurements with heavy-tailed noise variance [J]. Przegld Elektrotechniczny, 2017, 93(12): 24-27. |
[22] | DONG P, JING Z L, LEUNG H, et al. Variational Bayesian adaptive cubature information ˉlter based on Wishart distribution [J]. IEEE Transactions on Automatic Control, 2017, 62(11): 6051-6057. |
[23] | DONG P, JING Z L, LEUNG H, et al. Robust consensus nonlinear information ˉlter for distributed sensor networks with measurement outliers [J]. IEEE Transactions on Cybernetics, 2019, 49(10): 3731-3743. |
[24] | DONG P, JING Z L, SHEN K, et al. A distributed consensus ˉlter for sensor networks with heavy-tailed measurement noise [J]. Science China Information Sciences, 2018, 61(11): 119201. |
[25] | SHEN K, JING Z L, DONG P. A consensus nonlinear filter with measurement uncertainty in distributed sensor networks [J]. IEEE Signal Processing Letters,2017, 24(11): 1631-1635. |
[26] | S?ARKK?A S, HARTIKAINEN J. Non-linear noise adaptive Kalman ˉltering via variational Bayes[C]//IEEE International Workshop on Machine Learning for Signal Processing. Southampton, UK:IEEE, 2013: 1-6. |
[27] | S?ARKK?A S, NUMMENMAA A. Recursive noise adaptive Kalman ˉltering by variational Bayesian approximations [J]. IEEE Transactions on Automatic Control,2009, 54(3): 596-600. |
[28] | AGAMENNONI G, NIETO J I, NEBOT E M. Approximate inference in state-space models with heavytailed noise [J]. IEEE Transactions on Signal Processing, 2012, 60(10): 5024-5037. |
[29] | XU W J. Research on Bayesian filters based simultaneous localization and mapping algorithms for mobile robots [D]. Hangzhou, China: Zhejiang University,2016 (in Chinese). |
[30] | ZHANG S Y, DONG P, JING Z L. Adaptive cubature Kalman ˉltering SLAM algorithm based on variational Bayes [J]. Journal of Harbin Institute of Technology,2019, 51(4): 12-18 (in Chinese). |
[31] | WEINSTOCK R. Calculus of variations: With applications to physics and engineering [M]. New York,USA: Dover Publications, Inc, 1974. |
[32] | GELMAN A, CARLIN J B, STERN H S, et al.Bayesian data analysis [M]. London, UK: Chapman & Hall, 1995. |
[33] | KULLBACK S, LEIBLER R A. On information and sufficiency [J]. Annals of Mathematical Statistics, 1951,22(1): 79-86. |
[34] | CARVALHO C, WEST M. Dynamic matrix-variate graphical models [J]. Bayesian Analysis, 2007, 2(1):69-98. |
[35] | 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: IEEE, 2006: 3562-3568. |
[36] | MARTINEZ-CANTIN R, CASTELLANOS J A. Unscented SLAM for largescale outdoor environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Alberta, Canada:IEEE, 2005: 3427-3432. |
[37] | LI X, FENG Y B, HUANG R H, et al. The application of square-root cubature Kalman ˉlter in SLAM for underwater robot [C]//2017 Chinese Automation Congress (CAC). Jinan, china: IEEE, 2017: 2183-2187. |
[1] | DONG Xiangxiang, LÜ Runyan, CAI Yunze*. A Variational Bayes-Based Filter with Uncertain Heavy-Tailed Noise [J]. Journal of Shanghai Jiaotong University, 2020, 54(9): 881-889. |
[2] | WU Hao,CHEN Shuxin,HOU Zhiqiang,HUO Chenjie. A Robust Constrained Total Least Squares Algorithm for Passive Location [J]. Journal of Shanghai Jiaotong University, 2013, 47(07): 1114-1118. |
[3] | ZENG Wen-jing (曾文静), WAN Lei (万 磊), ZHANG Tie-dong (张铁栋), HUANG Shu-ling (黄蜀玲). Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using Looking Forward Sonar [J]. Journal of shanghai Jiaotong University (Science), 2012, 17(1): 91-097. |
Viewed | ||||||||||||||||||||||||||||||||||||||||||||||||||
Full text 82
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||
Abstract |
|
|||||||||||||||||||||||||||||||||||||||||||||||||