一种基于多波束测深仪的水下同步定位与地图构建方法

doi:10.3969/j.issn.1000-1093.2020.07.016

摘要/Abstract

摘要： 数据库辅助导航系统（DBRNS）可以校正惯性导航系统（INS）的累积误差，是自主水下潜航器的重要辅助导航手段，但DBRNS的有效运行依赖于高分辨率的基准图。为解决这一问题，提出一种基于多波束测深仪的图优化同步定位与地图构建（SLAM）方法。该方法在通用图优化SLAM方法的前端部分增加误匹配检测模块，减少了错误闭环对结果造成的不良影响。此外，新方法将局部灰度值编码算法用于闭环检测模块以提高闭环检测的精度。基于真实海底地形图的仿真实验结果表明，相对于通用图优化SLAM方法，采用新方法对INS轨迹进行校正可以取得更高的定位精度。

关键词: 自主水下潜航器, 同步定位与地图构建, 多波束测深仪, 惯性导航系统, 误匹配检测

Abstract: Database-referenced navigation systems(DBRNSs) are commonly used in autonomous underwater vehicles as underwater auxiliary navigation systems to correct the cumulative errors of inertial navigation systems. However, the DBRNS cannot run if a high resolution database cannot be obtained. A multi-beam sonar-based graph simultaneous localization and mapping (SLAM) method is proposed. A mismatch detection module is added to the front end to reduce the negative impact caused by the false closed-loop detection results. In the proposed method, the local gray value coding algorithm is used for the closed-loop detection module in order to improve the detection accuracy. Simulations based on the actual seabed terrain base map show that, compared with the general graph SLAM method, the proposed method can achieve higher positioning accuracy. Key

Key words: autonomousunderwatervehicle, simultaneouslocalizationandmapping, multi-beamsonar, inertialnavigationsystem, mismatchdetection

中图分类号:

U674.91

戴天，缪玲娟，邵海俊. 一种基于多波束测深仪的水下同步定位与地图构建方法[J]. 兵工学报, 2020, 41(7): 1384-1392.

DAI Tian， MIAO Lingjuan， SHAO Haijun. A Multi-beam Sonar-based Graph SLAM Method[J]. Acta Armamentarii, 2020, 41(7): 1384-1392.

参考文献

［1］ PAULL L, SAEEDI S, SETO M, et al. AUV navigation and localization: a review［J］. IEEE Journal of Oceanic Engineering, 2014, 39(1): 131-149.
［2］ WTNN R B, HUVENNE V A I, BAS T P L, et al. Autonomous underwater vehicles (AUVs): their past, present and future contributions to the advancement of marine geoscience［J］. Marine Geology, 2014, 352(2): 451-468.
［3］ NEMRA A, AOUF N. Robust INS/GPS sensor fusion for UAV localization using SDRE nonlinear filtering［J］. IEEE Sensors Journal, 2010, 10(4): 789-798.
［4］ STUTTERS L, LIU H, TILTMAN C, et al. Navigation technologies for autonomous underwater vehicles［J］. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 2008, 38(4): 581-589.
［5］ BISHOP G C. Gravitational field maps and navigational errors ［unmanned underwater vehicles］［J］. IEEE Journal of Oceanic Engineering, 2002, 27(3): 726-737.
［6］ CANCIANI A, RAQUET J. Airborne magnetic anomaly navigation［J］. IEEE Transactions on Aerospace & Electronic Systems, 2017, 53(1): 67-80.
［7］ CLAUS B, BACHMAYER R. Terrain-aided navigation for an underwater glider［J］. Journal of Field Robotics, 2015, 32(7): 935-951.
［8］ DURRANT W H, BAILEY T. Simultaneous localization and mapping: part I［J］. IEEE Robotics & Automation Magazine, 2006, 13(2): 99-110.
［9］ SAEEDI S, TRENTINI M, SETO M, et al. Multiple-robot simultaneous localization and mapping: a review［J］. Journal of Field Robotics, 2016, 33(1): 3-46.

［10］刘忠泽, 陈慧岩, 崔星, 等. 无人平台越野环境下同步定位与地图创建［J］. 兵工学报, 2019, 40(12): 2399-2406.
LIU Z Z, CHEN H Y, CUI X, et al. Real-time lidar slam in off-road environment for UGV［J］. Acta Armamentarii, 2019, 40(12): 2399-2406. (in Chinese)
［11］ 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.
［12］ THRUN S, MONTEMERLO M. The graph SLAM algorithm with applications to large-scale mapping of urban structures［J］. International Journal of Robotics Research, 2006, 25(5):403-429.
［13］ PUENTE P D L, RODRIGUEZ L D. Feature based graph-SLAM in structured environments［J］. Autonomous Robots, 2014, 37(3): 243-260.
［14］ MA T, LI Y, WANG R P, et al. AUV robust bathymetric simultaneous localization and mapping［J］. Ocean Engineering, 2018, 166: 336-349.
［15］李强, 张钹. 一种基于图像灰度的快速匹配算法［J］. 软件学报, 2006, 17(2): 216-222.
LI Q, ZHANG B. A fast matching algorithm based on image gray value［J］. Journal of Software, 2006, 17(2): 216-222. (in Chinese)
［16］ DAI T, MIAO L J, GUO Y B. A real-time mismatch detection method for underwater database-referenced navigation［J］.Sensors, 2019, 19(2): 307.
［17］ DAI T, MIAO L J, SHAO H J, et al. Solving gravity anomaly matching problem under large initial errors in gravity aided navigation by using an affine transformation based artificial bee colony algorithm［J］. Frontiers in Neurorobotics, 2019, 13(19): 1-11.
［18］ GRISETTI G, KU M R, STACHNISS C, et al. A tutorial on graph-based SLAM［J］. IEEE Intelligent Transportation Systems Magazine, 2010, 2(4): 31-43.
［19］ HARRIS P T, WHITEWAY T. Global distribution of large submarine canyons: geomorphic differences between active and passive continental margins［J］. Marine Geology, 2011, 285(1/2/3/4): 69-86.

第41卷第7期2020 年7月
兵工学报ACTA ARMAMENTARII
Vol.41No.7Jul.2020

[1]	张羽，王朋，刘纪元，钟荣兴，韦琳哲，迟骋. 合成孔径声纳重叠相位中心与惯性导航系统联合估计运动误差算法[J]. 兵工学报, 2021, 42(3): 588-597.
[2]	沈世斌，谢非，赵静，钱伟行，康国华，刘锡祥. 基于相位控制的惯性与卫星超紧组合导航系统信号解调方法[J]. 兵工学报, 2020, 41(3): 495-506.
[3]	胡杰，严勇杰，王子卉. 基于速度约束与模糊自适应滤波的车载组合导航[J]. 兵工学报, 2020, 41(2): 231-238.
[4]	王琪，汪立新，梁树晖，秦伟伟，沈强. 基于可观测性分析的混合式惯性导航系统连续自标定模型选择[J]. 兵工学报, 2020, 41(1): 56-67.
[5]	朱兵，许江宁，吴苗，李京书，何泓洋，李峰. 基于逆向导航算法的捷联式惯性导航系统改进优化对准方法[J]. 兵工学报, 2019, 40(9): 1860-1870.
[6]	董春梅，任顺清，陈希军，李巍. 一种激光陀螺捷联惯性导航系统级标定方法[J]. 兵工学报, 2019, 40(8): 1618-1626.
[7]	徐昊玮，廉保旺，刘尚波. 基于滑动窗迭代最大后验估计的多源组合导航因子图融合算法[J]. 兵工学报, 2019, 40(4): 807-819.
[8]	李群生，赵剡，王进达. 一种适用于高动态强干扰环境的视觉辅助微机械捷联惯性导航系统/全球定位系统超紧组合导航系统[J]. 兵工学报, 2019, 40(11): 2241-2249.
[9]	刘生攀, 王文举, 饶兴桥. 单轴旋转捷联惯性导航系统误差分析与转位方案研究[J]. 兵工学报, 2018, 39(9): 1749-1755.
[10]	李伟，姜俊峰，任海波，张光辉. 行进间火炮指向稳定跟踪系统建模与控制[J]. 兵工学报, 2018, 39(8): 1479-1490.
[11]	王蕾，王玮，刘增军，宋天骁. 混合式惯性导航系统全数字平台技术研究[J]. 兵工学报, 2018, 39(7): 1316-1322.
[12]	周召发，胡文，张志利，徐梓皓，陈河. 一种新的捷联惯性导航系统姿态四元数方程求解方法[J]. 兵工学报, 2018, 39(3): 511-518.
[13]	闵艳玲，熊智，邢丽，刘建业，殷德全. 基于对偶四元数的惯性/卫星/天文组合导航系统改进联邦滤波方法[J]. 兵工学报, 2018, 39(2): 315-324.
[14]	查峰，覃方君，李京书，叶斌. 三轴旋转惯性导航系统的旋转控制建模研究[J]. 兵工学报, 2017, 38(8): 1610-1618.
[15]	高军强，汤霞清，黄湘远，程旭维. 减振系统造成捷联惯性导航误差的有限元分析方法[J]. 兵工学报, 2016, 37(9): 1570-1577.