Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211200088-5.doi: 10.11896/jsjkx.211200088

• Interdiscipline & Application • Previous Articles     Next Articles

New SLAM Method of Multi-layer Lidar Assisted by Rotational Strapdown Inertial NavigationSystem

LYU Run1,2, LI Guan-yu3, QI Pei3, QIAN Wei-xing3, WANG Lan-ze3, FENG Tai-ping1,2   

  1. 1 Nari Group Corporation/State Grid Electric Power Research Institute,Nanjing 211106,China
    2 NARI-TECH Nanjing Control Systems Ltd.,Nanjing 211106,China
    3 School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210046,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:LYU Run,born in 1984,postgraduate.His main research interests include electronic information engineering and industrial robot kinematics
    LI Guan-yu,born in 1995,postgra-duate.His main research interests include lidar SLAM,multi-sensor data fusion algorithm for location.
  • Supported by:
    Foundation of Jiangsu Open Laboratory of Major Scientific Instrument and Equipment,Nanjing Normal University.

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Abstract: Focusing on the influence of low-accuracy inertial sensor on the performance of lidar/inertial SLAM,an optimized SLAM method by fusing information of multi-layer lidar and rotational strapdown inertial navigation system is studied.In this scheme,the rotating strapdown inertial navigation alignment method based on fuzzy adaptive Kalman filter is discussed,and the real-time correction of carrier attitude and inertial sensor error is completed in the process of carrier motion.Further more,the corrected inertial sensor data and LIDAR point cloud data are fused in tight coupling mode to improve the accuracy and real-time of positioning and mapping when the carrier moves in complex scenes.Experimental results show that the slam scheme based on rotating inertial navigation and multi-layer lidar information fusion not only ensures the real-time operation,but also effectively improves the positioning performance of lidar / inertial odometry and the accuracy of point cloud map.

Key words: Rotational inertial navigation system, Fuzzy adaptive kalman filter, Multi-layer lidar, Synchronous positioning and mapping, Lidar/inertial odometry

CLC Number: 

  • TP391
[1]ANDERSO S,BARFOOT T D.RANSAC for motion-distorted 3D visual sensors[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Tokyo,2013:2093-2099.
[2]BEHLEY J,STACHNISS C.Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments[C]//Robotics:Science and Systems.2018:1467-1479.
[3]ZHANG J,SINGH S.LOAM:Lidar Odometry and Mapping in Real-time[C]//Robotics:Science and Systems.2014:761-775.
[4]SHAN T,ENGLOT B.LeGO-LOAM:Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Madrid.2018:4758-4765.
[5]HUAI Z,HUANG G.Robocentric Visual-Inertial Odometry[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Madrid,2018:6319-6326.
[6]QIN T,LI P,SHEN S.Vins-mono:A robust and versatile monocular visual-inertial state estimator[J].IEEE Transactions on Robotics,2018,34(4):1004-1020.
[7]LEUTENEGGER S,LYNEN S,BOSSE M,et al.Keyframe-based visual-inertial odometry using nonlinear optimization[J].The International Journal of Robotics Research,2015,34(3):314-334.
[8]HUANG G P,TRAWNY N,MOURIKIS A I,et al.Observability-based consistent EKF estimators for multi-robot cooperative localization[J].Autonomous Robots,2011,30(1):99-122.
[9]PARK C,MOGHADAM P,KIM S,et al.Elastic LiDAR Fu-sion:Dense Map-Centric Continuous-Time SLAM[C]//IEEE International Conference on Robotics and Automation.Brisbane,2018:1206-1213.
[10]GENEVA P,ECKENHOFF K,YANG Y,et al.LIPS:LiDAR-Inertial 3D Plane SLAM[C]//IEEE/RSJ International Confe-rence on Intelligent Robots and Systems.Madrid,2018:123-130.
[11]FORSTER C,CARLONE L,DELLAERT F,et al.IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation[C]//Robotics:Science and Systems.Sapienza Univ,2015:236-252.
[12]YE H,CHEN Y,LIU M.Tightly Coupled 3D Lidar Inertial Odometry and Mapping[C]//International Conference on Robotics and Automation.Montreal:IEEE,2019:3144-3150.
[13]HAO S Y,LU H,WEI X,et al.Reduced high-degree strong tracking cubature Kalman filter and its application in integrated navigation system[J].Control and Decision,2019,34(10):2105-2114.
[14]QIAN W X.Research on High-Precision Initial Alignment ofStrapdown Inertial and Integrated Navigation System[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2010.
[15]WANG H P,CAI Y W,XIN C J,et al.Research on InitialAlignment Method Based on Rotation Modulation with Static Base[J].Journal of Ordnance Equipment Engineering,2020,41(7):128-132.
[16]HU J,SHI X Z.Refined Alignment Method for Single-axis Rotary Inertial Navigation Based on Fuzzy Adaptive Filtering[J].Journal of System Simulation,2021,33(2):315-323.
[17]XU Q J.Research on Calibration and Initial Alignment Techno-logy of Strapdown Inertial Navigation[D].Harbin:Harbin Engineering University,2014.
[18]JING Z Y.Research on Error Compensation Technology of Rotary Inertial Navigation System Based on MEMS Device[D].Taiyuan:North University of China,2020.
[19]LI W H.Lightweight Multisensor Integrated slam system Based on eskf and graph optimization [J].Scientific and Technological Innovation,2021(8):15-18.
[20]SHAUKAT N,ALI A,JAVED IQBAL M,et al.Multi-SensorFusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter[J].Sensors,2021,21(4):1149.
[21]JIA X H,XU W F,LIU J Y,et al.Solving method of lidarodometry based on IMU[J].Chinese Journal of Scientific Instrument,2021,42(1):39-48.
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