因子图发展及其在定位与导航的应用技术

周雅婧; 曾庆化; 刘建业; 孙克诚

doi:DOI:10.13442/j.gnss.1008-9268.2020.01.001

因子图发展及其在定位与导航的应用技术

doi: DOI:10.13442/j.gnss.1008-9268.2020.01.001

周雅婧^1,,
曾庆化^{1, 2, 3},
刘建业^{1, 2, 3},
孙克诚¹

1.
南京航空航天大学导航研究中心,江苏南京 211106
2.
先进飞行器导航、控制与健康管理工业和信息化部重点实验室,江苏南京 211106
3.
卫星通信与导航江苏高校协同创新中心,江苏南京 210016

详细信息

作者简介:
周雅婧 (1994—),女,硕士研究生,研究方向为多源信息融合导航．

通讯作者:
周雅婧 E-mail: zhouyajing@nuaa.edu.cn

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- 被引次数: 0
出版历程
- 刊出日期: 2020-02-15

Development of factor graph and its application technology in positioning and navigation

ZHOU Yajing^1
,,
ZENG Qinghua^{1, 2, 3},
LIU Jianye^{1, 2, 3},
SUN Kecheng¹

1.
Navigation Research Center, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China
2.
Key Laboratory of the MIIT advanced aircraft navigation, control and health management,Nanjing 211106, China
3.
Jiangsu University Collaborative Innovation Center for satellite communication and navigation,Nanjing 211106, China

摘要

摘要: 因子图作为一种表示因式分解的建模工具,在编码领域、统计学、信号处理和人工智能领域有着广泛的应用．因子图在导航领域的应用研究逐步发展起来．与单一导航系统对比,组合导航系统能够提供更精确、更具鲁棒性的导航结果,但是因其各个子系统的误差特性与工作频率不同的特点,增加了导航系统的设计复杂性．基于因子图的组合导航算法可以有效解决导航信息融合中的传感器异步问题且实现对多传感器的灵活配置,使得系统具有即插即用的特性,在非线性量测条件下可以获得较好效果.导航系统中的状态估计以及信息融合问题可以使用因子图模型表示,基于因子图的和积算法是组合导航信息融合的主要算法.本文对因子图及其在导航系统中的应用进行了探讨,主要包括:1)因子图的数学理论基础及其相关应用领域;2)因子图在定位与导航领域的发展和应用.
- 因子图 /
- 信息融合 /
- SLAM /
- 组合导航系统 /
- 和-积算法
Abstract: Factor graphs are widely used in the fields of coding, statistics, signal processing, and artificial intelligence as a modeling tool that represents factorization. The application of factor graphs in the navigation field is also gradually developed. The combination of multiple sensor information provides a more accurate and robust navigation state estimate than the information from a single sensor. However, various sensors have different error characteristics and these sensors usually operate at different frequencies. Considering that some sensors cannot supply the linear measurement information, and it brings the challenges to the design of the integrated navigation system. The navigation algorithm based on the factor graph model enables the system to have plug-and-play characteristics and achieve better results under nonlinear measurement conditions. The state estimation and information fusion problems in the navigation system can be represented by the factor graph model. Sum-product algorithm based on factor graph is the main method in navigation and positioning system.This paper summarizes the factor graph theory and its application in the navigation system, including: 1) Mathematical theoretical basis of factor graph and its related application fields 2) Development and application of factor graphs in the field of positioning and navigation.
- factor graph /
- information fusion /
- SLAM /
- integrated navigation /
- sum-product algorithm

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参考文献(57)

[1]	LOELIGER H A. An introduction to factor graphs［J］. IEEE Signal Processing Magazine, 2004, 21(1):28-41.DOI: 10.1109/MSP.2004.1267047.
[2]	TANNER R. A recursive approach to low complexity codes［J］. IEEE Transactions on Information Theory, 1981, 27(5):533-547.DOI: 10.1109/TIT.1981.1056404.
[3]	WIBERG N, LOELIGER H A, KOTTER R. Codes and iterative decoding on general graphs［J］. European Transactions on Telecommunications, 1995, 6(5):513-525.DOI: 10.1002/ett.4460060507.
[4]	KSCHISCHANG F R, FREY B J， LOELIGER H A. Factor graphs and the sumproduct algorithm［J］.IEEE Transactions on Information Theory,2001,47(2):498-519.DOI: 10.1109/18.910572.
[5]	LOELIGER H -A. Least squares and Kalman filtering on forney graphs［C］／／Codes, Graphs, and Systems, Berlin:Springer, 2002:113-115.
[6]	李建平,王宏远,蔡德钧.因子图原理及其应用前景［J］.电讯技术,2000,40(2):20-24.
[7]	INDELMAN V, WILLIAMS S, KAESS M, et al. Information fusion in navigation systems via factor graph based incremental smoothing［J］. Robotics and Autonomous Systems, 2013, 61(8):721-738.DOI: 10.1016/j.robat.2013.05.001.
[8]	张宏毅,王立威,陈瑜希.概率图模型研究进展综述［J］.软件学报,2013,24(11):2476-2497.
[9]	FREY B J. Extending factor graphs so as to unify directed and undirected graphical models［C］／／Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence,2002:257-264.
[10]	朱联祥, 杨士中, 汪纪锋. 基于因子图的Turbo码译码［J］. 重庆大学学报(自然科学版), 2002, 25(7):40-44.
[11]	巩克现,董政,林华明,等.载波相位同步算法中因子图模型实现方法［J］.信息工程大学学报,2012,13(4):432-437.
[12]	GUO Q H, PING L. LMMSE turbo equalization based on factor graphs［J］. IEEE Journal on Selected Areas in Communications, 2008, 26(2):311-319.DOI: 10.1109/JSAC.2008.080208.
[13]	HASELMAYR W, ETZLINGER B, SPRINGER A. Equalization of MIMO-ISI channels based on Gaussian message passing in factor graphs［C］／／2012 7th International Symposium on Turbo Codes and Iterative Information Processing(ZSTC)， 2012.DOI: 10.1109/ISTC.2012.6325202.
[14]	GHAHRAMANI Z,JORDAN M. Factorial hidden Markov models［J］. Machine Learning, 1997, 29(2-3):245-273.
[15]	吕香玲,张志勇,胡光岷.基于因子图——和积算法的故障链路诊断［J］.计算机应用,2012,32(2):343-346.
[16]	沈毅,张筱磊,王振华.基于EMD和有向因子图的航天器故障诊断［J］.哈尔滨工业大学学报,2013,45(1):19-24.
[17]	朱翠涛,杨凡,汪汉新,等.基于因子图的分布式变分稀疏贝叶斯压缩感知［J］.通信学报,2014,35(1):140-147.
[18]	TA D N, KOBILAROV M, DELLAERT F. A factor graph approach to estimation and model predictive control on unmanned aerial vehicles［C］／／International Conference on Unmanned Aircraft Systems, 2014.
[19]	陆一，魏东岩，米奇峰. 一种改进的因子图加权融合算法［C］// 第七届中国卫星导航学术年会论文集,2016.
[20]	陈恩庆,肖素珍,高新利.因子图在卫星姿态估计中的应用［J］.计算机仿真,2015,32(6):63-66.
[21]	顾智宇,秦涛,王斌.一种基于因子图的搜索广告转化预测模型［J］.中文信息学报,2015,29(3):140-149.
[22]	ELFES A. Sonar-based real-world mapping and navigation［J］. IEEE Journal on Robotics and Automation, 1987, 3(3):249-265.DOI: 10.1109/JRA.1987.1087096.
[23]	王录涛,吴林峰.基于图优化的视觉SLAM研究进展与应用分析［J］.计算机应用研究,2020,37(1):9-15.
[24]	[KG*2]BEN E Y, INDELMAN V. Active online visualinertial navigation and sensor calibration via belief space planning and factor graph based incremental smoothing［C］// IEEE／RSJ International Conference on Intelligent Robots and Systems (IROS), 2017:2616-2622.
[25]	DENNIS J E, SCHNABEL R I. Numerical methods for unconstrained optimization and nonlinear equations［M］. Prentice-Hall, 1983.DOI: 10.2307/2288097.
[26]	WHELAN T, KAESS M, LEONARD J J, et al. Deformation-based loop closure for large scale dense RGB-D SLAM［C］／／IEEE／RSJ International Conference on Intelligent Robots and Systems, 2013.DOI: 10.1109/IROS.2013.6696405.
[27]	WHELAN T, KAESS M, JOHANNSSON H, et al. Real-time large-scale dense RGB-D SLAM with volumetric fusion［J］. The International Journal of Robotics Research, 2015, 34(4-5):598-626.
[28]	SALAS-MORENO R F, NEWCOMBE R A, STRASDAT H, et al. SLAM++: Simultaneous localisation and mapping at the level of objects［C］／／IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2013.DOI: 10.1109/CVPR.2013.178.
[29]	TREVOR A J B, ROGERS J G, CHRISTENSEN H I. Planar surface SLAM with 3D and 2D sensors［C］／／IEEE International Conference on Robotics and Automation, 2012.DOI: 10.1109/ICRA.2012.6225287.
[30]	KAESS M. Simultaneous localization and mapping with infinite planes［C］／／IEEE International Conference on Robotics and Automation(ICRA),2015.DOI: 10.1109/ICRA.2015.7139837.
[31]	HSIAO M, WESTMAN E, ZHANG G, et al. Keyframebased dense planar SLAM［C］／／IEEE International Conference on Robotics and Automation, 2017.DOI: 10.1109/ICRA.2017.7989597.
[32]	HOVER F S, EUSTICE R M, KIM A, et al. Advanced perception, navigation and planning for autonomous in-water ship hull inspection［J］. The International Journal of Robotics Research, 2012, 31(12):1445-1464.DOI: 10.1177/0278364912461059.
[33]	TEIXEIRA P V, KAESS M, HOVER F S, et al. Underwater inspection using sonar-based volumetric submaps［C］／／IEEE／RSJ International Conference on Intelligent Robots and Systems, 2016.
[34]	VANMIDDLESWORTH M, KAESS M, HOVER F, et al. Mapping 3D underwater environments with smoothed submaps［M］// MEJIAS L, CORKE P,ROBERTS J. Field and Service Robotics：Springer Tracts in Advanced Robotics Vol 105,Springer, Cham. 2015:17-30.
[35]	KIM A, EUSTICE R M. Perception-driven navigation: Active visual SLAM for robotic area coverage［C］／／Proceedings of IEEE International Conference on Robotics and Automation, 2013.DOI: 10.1109/ICRA.2013.6631022.
[36]	OZOG P, TRONI G, KAESS M, et al. Building 3D mosaics from an autonomous underwater vehicle, Doppler velocity log, and 2D imaging sonar［C］// Proceedings of IEEE International Conference on Robotics and Automation, 2015:1137-1143.DOI: 10.1109/ICRA.2015.7139334.
[37]	CARLEVARIS-BIANCO N, KAESS M, EUSTICE R M. Generic node removal for factorgraph SLAM［J］. IEEE Transactions on Robotics, 2014, 30(6):1371-1385.DOI: 10.1109/TRO.2014.2347571.
[38]	BEALL C, DELLAERT F, MAHON I, et al. Bundle adjustment in large-scale 3D reconstructions based on underwater robotic surveys［C］／／Oceans IEEE, 2011.DOI:10.1109/Oceans Spain 2011.6003631.
[39]	BICHUCHER V, WALLS J M, OZOG P, et al. Bathymetric factor graph SLAM with sparse point cloud alignment［C］／／Oceans IEEE,2015. DOI: 10.23919/OCEANS.2015.7404433.
[40]	TWEDDLE B E, SAENZ-OTEREO A, LEONARD J J, et al. Factor graph modeling of rigid-body dynamics for localization, mapping, and parameter estimation of a spinning object in space［J］. Journal of Field Robotics, 2014, 32(6).DOI: 10.1002/rob.21548.
[41]	ZHANG J, KAESS M,SINGH S. A real-time method for depth enhanced visual odometry［J］. Autonomous Robots, 2017, 41(1):31-43.DOI: 10.1007/S10514-015-9525-1.
[42]	ANDERSON S, BARFOOT T D,TONG C H, et al.Batch nonlinear continuoustime trajectory estimation as exactly sparse Gaussian process regression［J］. Autonomous Robots, 2015, 39(3):221-238.DOI: 10.10.1007/S10514-015-9455-y.
[43]	ANDERSON S, DELLAERT F, BARFOOT T D. A hierarchical wavelet decomposition for continuoustime SLAM［C］／／IEEE International Conference on Robotics and Automation, 2014.DOI: 10.1109/ICRA.2014.6906884.
[44]	YAN X Y, INDELMAN V, BOOTS B. Incremental sparse GP regression for continuous-time trajectory estimation and mapping［J］. Robotics and Autonomous Systems, 2015(87):120-132.DOI:10.1016/j.robot 2016.10.004.
[45]	TANG C K, ZHANG L L,ZHANG Y, et al.Factor graph aided distributed multi-navigation cooperative positioning algorithm［C］／／ION GNSS+, 2018.DOI: 10.33012/2018.15957.
[46]	INDELMAN V, WILLIAMS S, KAESS M, et al. Factor graph based incremental smoothing in inertial navigation systems［C］／／15th International Conference on Information Fusion, 2012.
[47]	INDELMAN V, WILLIAMS S, KAESS M, et al. Information fusion in navigation systems via factor graph based incremental smoothing［J］. Robotics and Autonomous Systems, 2013, 61(8):721-738.DOI: 10.1016/j.robot.2013.05.001.
[48]	LUPTON T, SUKKARIEH S. Visualinertialaided navigation for high-dynamic motion in built environments without initial conditions［J］. IEEE Transactions on Robotics, 2012, 28(1):61-76.DOI: 10.1109/TRO.2011.2170332.
[49]	CHRISTIAN F, LUCA C, FRANK D,et al. IMU preintegration on manifold for efficient visualinertial maximum-a-posteriori estimation［R］. 2015.DOI: 10.15607/RSS.2015.X1.006.
[50]	LEUTENEGGER S, LYNEN S, BOSSE M, et al. Keyframe-based visual-inertial odometry using nonlinear optimization［J］. The International Journal of Robotics Research, 2014, 34(3):314-334.DOI: 10.1177/0278364914554813.
[51]	USENKO V, ENGEL J, STUCKLER J, et al. Direct visual-inertial odometry with stereo cameras［C］／／IEEE International Conference on Robotics and Automation (ICRA), 2016.DOI: 10.1109/ICRA.2016.7487335.
[52]	王慧哲. 基于多信息融合的无人机全源导航关键技术研究［D］.南京：南京航空航天大学,2017.
[53]	ZENG Q H, CHEN W N, LIU J Y, et al. An improved multi-sensor fusion navigation algorithm based on the factor graph［J］. Sensors, 2017, 17(3):641-648.DOI: 10.3390/S17030641.
[54]	CHIU H P, WILLIAMS S, DELLAERT F, et al. Robust vision-aided navigation using Sliding-Window Factor graphs.［C］／／IEEE International Conference on Robotics and Automation, 2013.DOI: 10.1109/ICRA.2013.6630555.
[55]	高军强,汤霞清,张环,等.基于因子图的车载INS／GNSS／OD组合导航算法［J］.系统工程与电子技术,2018,40(11):2547-2553.
[56]	高军强, 汤霞清, 张环, 等. 基于因子图算法的INS／GPS信息滞后处理方法［J］. 计算机应用, 2018, 38(11):3342-3347.
[57]	朱晓晗,陈帅,蒋长辉, 等. 基于因子图的组合导航方法及其可行性研究［J］. 电光与控制, 2019, 26(4):66-70.