拒止环境下视觉辅助定位与导航技术综述

廖鑫淼; 王正平; 刘莉; 贺云涛

doi:10.15892/j.cnki.djzdxb.2020.06.018

弹箭与制导学报 >

2020 , Vol. 40 >Issue 6: 76 - 81

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2020.06.018

拒止环境下视觉辅助定位与导航技术综述

廖鑫淼 ,
王正平 ,
刘莉 ,
贺云涛

展开

北京理工大学宇航学院,北京 100081

廖鑫淼(1996-),女,北京密云人,硕士研究生,研究方向:飞行器控制。

收稿日期: 2020-01-15

网络出版日期: 2025-05-30

基金资助

2018年度红箭创新基金项目资助

收起

Survey on Vision-assisted Positioning and Navigation Technology in Denied Environment

LIAO Xinmiao ,
WANG Zhengping ,
LIU Li ,
HE Yuntao

Expand

School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China

Received date: 2020-01-15

Online published: 2025-05-30

Fold

摘要

当前导航系统严重依赖GNSS信号,导致武器系统在拒止环境下无法工作,视觉导航是一种新型的可以替代GNSS的导航方法。通过分析视觉导航的实现方式以及不同传感器方案的适用场景,得出单目视觉SLAM方法适合在野外作战环境提供导航信息的结论。针对单目SLAM算法,分别综述基于滤波器与基于最优化两类算法的研究现状。介绍视觉惯性组合导航的优势与研究发展现状,最后指出视觉组合导航是导航算法完善化、智能化的前进方向。

关键词： 拒止环境; 视觉导航; 同时定位与建图; 视觉组合导航

本文引用格式

廖鑫淼 , 王正平 , 刘莉 , 贺云涛 . 拒止环境下视觉辅助定位与导航技术综述[J]. 弹箭与制导学报, 2020 , 40(6) : 76 -81 . DOI: 10.15892/j.cnki.djzdxb.2020.06.018

Abstract

The current navigation system relies heavily on GNSS signals, which makes the weapon system unable to work in a denial environment. Visual navigation is a new type of navigation method that can replace GNSS. By analyzing the realization of visual navigation and the applicable scenarios of different sensor solutions, it is concluded that the monocular visual SLAM method is suitable for providing navigation information in the field combat environment. For the monocular SLAM algorithm, the research status of the two types of algorithms based on filter and optimization is reviewed respectively. This paper introduces the advantages and research status of visual inertial integrated navigation, and finally point out that visual integrated navigation is the way forward for the perfection and intelligence of navigation algorithms.

Key words： denied environment; visual navigation; SLAM; visual integrated navigation

[an error occurred while processing this directive]

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

[1]	刘冰. 提升卫星信号在拒止环境下导航定位能力的技术研究[J]. 通讯世界, 2017(5): 114.

[2]	赵岩, 高关根, 叶继坤, 等. 抗拒止环境的全源导航子系统模型综述[J]. 导航定位学报, 2018, 6(4): 1-6.

[3]	GUO P Y, LI X, GUI Y, et al. Airborne vision-aided landing navigation system for fixed-wing UAV[C]// IEEE. Proceedings of the 12th International Conference on Signal Processing. New York: IEEE, 2014: 1215-1220.

[4]	温熙, 郭杭, 魏静波. 视觉组合导航系统方法的综述[C]// 中国卫星导航定位协会. 第三届中国卫星导航与位置服务年会暨展览会. 北京: 测绘出版社, 2014: 214-217.

[5]	朱凯, 刘华峰, 夏青元. 基于单目视觉的同时定位与建图算法研究综述[J]. 计算机应用研究, 2018, 35(1): 1-6.

[6]	DURRANT-WHYTE H, BAILEY T. Simultaneous localization and mapping: part I[J]. IEEE Robotics and Automation Magazine, 2006, 13(2): 99-110.

[7]	顾照鹏, 刘宏. 单目视觉同步定位与地图创建方法综述[J]. 智能系统学报, 2015, 10(4): 499-507.

[8]	WANG D B, LIANG H W, MEI T, et al. Lidar scan matching EKF-SLAM using the differential model of vehicle motion[C]// IEEE. Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV). New York: IEEE, 2013: 908-912.

[9]	DAVISON A J, MAYOL W W, MURRAY D W. Real-time localization and mapping with wearable active vision[C]// IEEE. Proceedings of the second IEEE and ACM International Symposium on Mixed and Augmented Reality. New York: IEEE, 2003: 18-27.

[10]	DAVISON A J, CID Y G, KITA N. Real-time 3d SLAM with wide-angle vision[J]. IFAC Proceedings Volumes, 2004, 37(8):868-873.

[11]	DAVISON A J, REID I D, MOLTON N D, et al. MonoSLAM: real-time single camera SLAM[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 1052-1067. PMID

[12]	LEMAIRE T, LACROIX S, SOLA J. A practical 3D bearing-only SLAM algorithm[C]// IEEE. Proceedings of the 2005 IEEE and RSJ International Conference on Intelligent Robots and Systems. New York: IEEE, 2005: 2449-2454.

[13]	ROUSSILLON C, SOLA J, CODOL J M, et al. RT-SLAM: a generic and real-time visual SLAM Implementation[C]// ICVS. Proceedings of the 8th International Conference of Computer Vision Systems. Berlin: Springer, 2011: 20-22.

[14]	GIREMUS A, DOUCET A, CALMETTES V, et al. A Rao-Blackwellized particle filter for INS/GPS integration[C]// IEEE. Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing. New York: IEEE, 2004: 961-964.

[15]	MONTEMERLO M, THRUN S, KOLLER D, et al. FastSLAM: a factored solution to the simultaneous localization and mapping problem[C]// ACM. Proceedings of the 18th National Conference on Artificial Intelligence. California: American Association for Artificial Intelligence, 2002: 593-598.

[16]

MONTEMERLO

, THRUN

, KOLLER

, et al. FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges[C]// ACM. Proceedings of the 18th International Joint Conference on Artificial Intelligence. San Francisco: Morgan Kaufmann Publisher Inc, 2002: 1151-1156.

[17]	HOLMES S A, KLEIN G, MURRAY D W. An O(N ²) square root unscented Kalman filter for visual simultaneous localization and mapping[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(7): 1251-1263.

[18]	THRUN S, LIU Y F, KOLLER D, et al. Simultaneous localization and mapping with sparse extended information filters[J]. The International Journal of Robotics Research, 2004, 23(7): 693-716.

[19]	GEORG K, DAVID M. Parallel tracking and mapping for small AR workspaces[C]// IEEE. Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality. New York: IEEE, 2007: 225-234.

[20]	MOURAGNON E, LHUILLIER M, DHOME M, et al. Monocular vision based SLAM for mobile robots[C]// IEEE. Proceedings of the 18th International Conference on Pattern Recognition. New York: IEEE, 2006: 1027-1031.

[21]	SILVEIRA G, MALIS E, RIVES P. An efficient direct approach to visual SLAM[J]. IEEE Transactions on Robotics, 2008, 24(5): 969-979.

[22]	NEWCOMBE R A, DAVISON A J. Live dense reconstruction with a single moving camera[C]// IEEE. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2010: 1498-1505.

[23]	STRASDAT H, MONTIEL J M M, DAVISON A J. Scale drift-aware large scale monocular SLAM[C]// Robotics: Science and Systems. Proceedings of Robotics:Science and Systems VI, Universidad de Zaragoza. Massachusetts: the MIT Press, 2011: 1-8.

[24]	PRETTO A, MENEGATTI E, PAGELLO E. Omnidirectional dense large-scale mapping and navigation based on meaningful triangulation[C]// IEEE. Proceedings of the 2011 IEEE International Conference on Robotics and Automation. New York: IEEE, 2011: 3289-3296.

[25]	NEWCOMBE R A, LOVEGROVE S J, DAVISON A J. DTAM: dense tracking and mapping in real-time[C]// IEEE. Proceedings of the 2011 International Conference on Computer Vision. New York: IEEE, 2011: 2320-2327.

[26]	FORSTER C, PIZZOLI M, SCARAMUZZA D. SVO: fast semi-direct monocular visual odometry[C]// IEEE. Proceedings of the 2014 IEEE International Conference on Robotics and Automation. New York: IEEE, 2014: 15-22.

[27]	HERRERA C D, KIM K, KANNALA J, et al. DT-SLAM: deferred triangulation for robust SLAM[C]// IEEE. Proceedings of the second International Conference on 3D Vision. New York: IEEE, 2014: 609-616.

[28]	ENGEL J, SCHOPS T, CREMERS D. LSD-SLAM: large-scale direct monocular SLAM[C]// EVVC. Proceedings of the 2014 European Conference on Computer Vision. Berlin: Springer, 2014: 834-849.

[29]	CONCHA A, CIVERA J. DPPTAM: dense piecewise planar tracking and mapping from a monocular sequence[C]// IEEE. Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. New York: IEEE, 2015: 5686-5693.

[30]	MUR-ARTAL R, MONTIEL J M M, TARDOS J D. ORB-SLAM: a versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2017, 31(5): 1147-1163.

[31]	SCHNEIDER T. Visual and inertial motion estimation and calibration[D]. Swiss: ETH Zurich, 2019.

[32]	BIJKER J, STEYN W. Kalman filter configurations for a low-cost loosely integrated inertial navigation system on an airship[J]. Control Engineering Practice, 2008, 16 (12): 1509-1518.

[33]	顾照鹏, 董秋雷. 基于部分惯性传感器信息的单目视觉同步定位与地图创建方法[J]. 计算机辅助设计与图形学学报, 2012, 24(2): 155-160.

[34]	LIU H, ZHOU Y L, GU Z P. Inertial measurement unit-camera calibration based on incomplete inertial sensor information[J]. Journal of Zhejiang University SCIENCE C: Computer and Electronics, 2014, 15(11): 999-1008.

[35]	MOURIKIS A I, ROUMELIOTIS S I. A multi-state constraint Kalman filter for vision-aided inertial navigation[C]// IEEE. Proceedings of the 2007 IEEE International Conference on Robotics and Automation. New York: IEEE, 2007: 3565-3572.

[36]	BLOESCH M, OMARI S, HUTTER M, et al. Robust visual inertial odometry using a direct EKF-based approach[C]// IEEE. Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. New York: IEEE, 2015: 298-304.

[37]	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.

[38]	MUR-ARTAL R, TARDOS J. Visualinertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803.

[39]	QIN T, LI P L, SHEN S J. VINS-Mono: a robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.

Options

文章导航