2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Lidar/IMU紧耦合的实时定位方法

李帅鑫 李广云 王力 杨啸天

李帅鑫, 李广云, 王力, 杨啸天. Lidar/IMU紧耦合的实时定位方法. 自动化学报, 2020, 46(x): 1−13 doi: 10.16383/j.aas.c190424
引用本文: 李帅鑫, 李广云, 王力, 杨啸天. Lidar/IMU紧耦合的实时定位方法. 自动化学报, 2020, 46(x): 1−13 doi: 10.16383/j.aas.c190424
Li Shuai-Xin, Li Guang-Yun, WANG Li, YANG Xiao-Tian. Lidar/IMU tightly coupled real-time localization method. Acta Automatica Sinica, 2020, 46(x): 1−13 doi: 10.16383/j.aas.c190424
Citation: Li Shuai-Xin, Li Guang-Yun, WANG Li, YANG Xiao-Tian. Lidar/IMU tightly coupled real-time localization method. Acta Automatica Sinica, 2020, 46(x): 1−13 doi: 10.16383/j.aas.c190424

Lidar/IMU紧耦合的实时定位方法

doi: 10.16383/j.aas.c190424
基金项目: 地理信息工程国家重点实验室基金(SKLGIE2018-M-3-1), 国家重点研发计划(2017YFF0206001), 国家自然科学基金(41501491)资助
详细信息
    作者简介:

    李帅鑫:战略支援部队信息工程大学地理空间信息学院博士研究生. 2015年获中南大学测绘工程学士学位, 2018年获战略支援部队信息工程大学控制科学与工程硕士学位. 主要研究方向为多传感器融合的SLAM, 移动测量. 本文通信作者. E-mail: lsx_navigation@sina.com

    李广云:战略支援部队信息工程大学地理空间信息学院教授、博导. 1987年获解放军测绘学院测绘科学与技术硕士学位. 主要研究方向为精密工程与工业测量, 导航应用及导航定位与位置服务. E-mail: guangyun_li@163.com

  •  收稿日期 2019-06-02    录用日期 2019-12-15 Manuscript received June 2, 2019; accepted December 15, 2019 地理信息工程国家重点实验室基金 (SKLGIE2018-M-3-1), 国家重点研发计划 (2017YFF0206001), 国家自然科学基金(41501491) 资助 Supported by State Key Laboratory of Geo-Information Engineering(SKLGIE2018-M-3-1), National Key Research and Development Project£2017YFF0206001), National Natural Science Foundation of China(41501491)
  •  本文责任编委 吴毅红 Recommended by Associate Editor WU Yi-Hong 1. 地理信息工程国家重点实验室 西安 710054    2. 战略支援部队信息工程大学地理空间信息学院 郑州 450001 1. State Key Laboratory of Geo-Information Engineering, Xi’an 710054    2. College of Geospatial Information, PLA Information Engineering University, Zhengzhou 450001
  • 1http://www.cvlibs.net/datasets/kitti/eval_odometry.php

Lidar/IMU Tightly Coupled Real-time Localization Method

Funds: Supported by State Key Laboratory of Geo-Information Engineering(SKLGIE2018-M-3-1), National Key Research and Development Project(2017YFF0206001), National Natural Science Foundation of China(41501491)
  • 摘要: 本文以实现移动小型智能化系统的实时自主定位为目标, 针对激光里程计误差累计大, 旋转估计不稳定, 以及观测信息利用不充分等问题, 提出一种Lidar/IMU紧耦合的实时定位方法—Inertial-LOAM. 数据预处理部分, 对IMU数据预积分, 降低优化变量维度, 并为点云畸变校正提供参考. 提出一种基于角度图像的快速点云分割方法, 筛选结构性显著的点作为特征点, 降低点云规模, 保证激光里程计的效率; 针对地图构建部分存在的地图匹配点搜索效率低和离散点云地图的不完整性问题, 提出传感器中心的多尺度地图模型, 利用环形容器保持地图点恒定, 并结合多尺度格网保证地图模型中点的均匀分布. 数据融合部分, 提出Lidar/IMU紧耦合的优化方法, 将IMU和Lidar构成的预积分因子、配准因子、闭环因子插入全局因子图中, 采用基于贝叶斯树的因子图优化算法对变量节点进行增量式优化估计, 实现数据融合. 最后, 采用实测数据评估Inertial-LOAM的性能并与LeGO-LOAM, LOAM和Cartographer对比. 结果表明, Inertial-LOAM在不明显增加运算负担的前提下大幅降低连续配准误差造成的误差累计, 具有良好的实时性; 在结构性特征明显的室内环境, 定位精度达厘米级, 与对比方法持平; 在开阔的室外环境, 定位精度达分米级, 而对比方法均存在不同程度的漂移.
    1)   收稿日期 2019-06-02    录用日期 2019-12-15 Manuscript received June 2, 2019; accepted December 15, 2019 地理信息工程国家重点实验室基金 (SKLGIE2018-M-3-1), 国家重点研发计划 (2017YFF0206001), 国家自然科学基金(41501491) 资助 Supported by State Key Laboratory of Geo-Information Engineering(SKLGIE2018-M-3-1), National Key Research and Development Project£2017YFF0206001), National Natural Science Foundation of China(41501491)
    2)   本文责任编委 吴毅红 Recommended by Associate Editor WU Yi-Hong 1. 地理信息工程国家重点实验室 西安 710054    2. 战略支援部队信息工程大学地理空间信息学院 郑州 450001 1. State Key Laboratory of Geo-Information Engineering, Xi’an 710054    2. College of Geospatial Information, PLA Information Engineering University, Zhengzhou 450001
    3)  1http://www.cvlibs.net/datasets/kitti/eval_odometry.php
  • 图  1  系统框架图

    Fig.  1  The overview of the system

    图  2  点云分割示例

    Fig.  2  Example of point cloud segmentation

    图  3  IMU与Lidar的频率关系

    Fig.  3  Frequencies of IMU and Lidar

    图  4  局部地图示意图

    Fig.  4  Demonstration for the local map

    图  5  因子图结构

    Fig.  5  Structure of the factor graph

    图  6  数据采集平台

    Fig.  6  Data collection platform

    图  10  Inertial-LOAM轨迹及建图结果

    Fig.  10  Trajectory and mapping result of Inertial-LOAM

    图  7  系统运行时间对比

    Fig.  7  Comparison of time cost of two systems

    图  8  闭环优化效果

    Fig.  8  Performance of loop optimization

    图  9  室外开阔环境IL/LL/L/C轨迹结果对比

    Fig.  9  Comparison of pose estimation of IL/LL/L/C in outdoor environment

    表  1  累计误差结果

    Table  1  Error accumulation result

    场景方法横滚(°)俯仰(°)航向(°)角度偏差(°)X方向(m)Y方向(m)Z方向(m)位置偏差(m)
    2#数据[11]IMU0.7481.0180.5981.39835.09584.652−665.782672.059
    Cartographer0.113−0.7090.9891.2220.4051.3170.6701.532
    LOAM0.0160.1410.9250.9360.3160.3490.0250.471
    LeGO-LOAM0.0610.0810.9160.9210.0680.3380.1150.364
    Inertial-LOAM0.0130.0260.9170.9180.0610.2580.0230.266
    室内环境Cartographer0.003−0.0010.0170.0170.0230.0370.0280.052
    LOAM0.0010.0040.0680.0680.0320.0830.0320.095
    LeGO-LOAM−0.006−0.002−0.0210.0220.0160.047−0.0320.059
    Inertial-LOAM−0.0080.001−0.0200.0210.0210.0430.0270.055
    室外环境Cartographer0.075−0.0240.0810.1131.7472.592−0.4493.158
    LOAM−0.0310.0060.0960.1010.04672.368−0.0652.353
    LeGO-LOAM−0.024−0.5430.0410.545−19.857−14.914−0.35524.836
    Inertial-LOAM0.006−0.0800.0030.080−0.310−0.100−0.0300.328
    下载: 导出CSV
  • [1] 李帅鑫. 激光雷达/相机组合的3D SLAM技术研究. 硕士学位论文, 战略支援部队信息工程大学, 2018.

    LI Shuai-Xin. Research on lidar/camera coupled 3d slam. Master’s thesis, PLA Information Engineering University, 2018.
    [2] Paul J Besl and Neil D McKay. Method for registration of 3-d shapes. In Sensor fusion IV: control paradigms and data structures, volume 1611, pages 586-606. International Society for Optics and Photonics, 1992.
    [3] 3 François Pomerleau, Francis Colas, Roland Siegwart, and Stéphane Magnenat. Comparing icp variants on real-world data sets. Autonomous Robots, 2013, 34(3): 133−148 doi: 10.1007/s10514-013-9327-2
    [4] 4 Hartmut Surmann, Andreas Nüchter, Kai Lingemann, and Joachim Hertzberg. 6d slampreliminary report on closing the loop in six dimensions. IFAC Proceedings Volumes, 2004, 37(8): 197−202 doi: 10.1016/S1474-6670(17)31975-4
    [5] Frank Moosmann and Christoph Stiller. Velodyne slam. In 2011 ieee intelligent vehicles symposium (iv), pages 393−398. IEEE, 2011.
    [6] 6 David Droeschel, Max Schwarz, and Sven Behnke. Continuous mapping and localization for autonomous navigation in rough terrain using a 3d laser scanner. Robotics and Autonomous Systems, 2017, 88: 104−115 doi: 10.1016/j.robot.2016.10.017
    [7] David Droeschel and Sven Behnke. Efficient continuous-time slam for 3d lidar-based online mapping. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 1−9. IEEE, 2018.
    [8] Ji Zhang and Sanjiv Singh. Loam: Lidar odometry and mapping in real-time. In Robotics: Science and Systems, volume 2, page 9, 2014.
    [9] 9 Ji Zhang and Sanjiv Singh. Low-drift and real-time lidar odometry and mapping. Autonomous Robots, 2017, 41(2): 401−416 doi: 10.1007/s10514-016-9548-2
    [10] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354−3361. IEEE, 2012.
    [11] Tixiao Shan and Brendan Englot. Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4758−4765. IEEE, 2018.
    [12] Wolfgang Hess, Damon Kohler, Holger Rapp, and Daniel Andor. Real-time loop closure in 2d lidar slam. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 1271−1278. IEEE, 2016.
    [13] 13 Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza. On-manifold preintegration for real-time visual-inertial odometry. IEEE Transactions on Robotics, 2016, 33(1): 1−21
    [14] 14 Yashar Balazadegan Sarvrood, Siavash Hosseinyalamdary, and Yang Gao. Visual-lidar odometry aided by reduced imu. ISPRS International Journal of Geo-Information, 2016, 5(1): 3 doi: 10.3390/ijgi5010003
    [15] Sebastian Thrun, Wolfram Burgard, and Dieter Fox. Probabilistic robotics. MIT press, 2005.
    [16] Sebastian Hening, Corey A Ippolito, Kalmanje S Krishnakumar, Vahram Stepanyan, and Mircea Teodorescu. 3d lidar slam integration with gps/ins for uavs in urban gps-degraded environments. In AIAA Information SystemsAIAA Infotech@ Aerospace, page 0448. 2017.
    [17] 17 Frank Dellaert, Michael Kaess, et al. Factor graphs for robot perception. Foundations and Trends® in Robotics, 2017, 6(1-2): 1−139 doi: 10.1561/2300000043
    [18] 18 Stefan Leutenegger, Simon Lynen, Michael Bosse, Roland Siegwart, and Paul Furgale. Keyframe-based visual-inertial odometry using nonlinear optimization. The International Journal of Robotics Research, 2015, 34(3): 314−334 doi: 10.1177/0278364914554813
    [19] Kurt Konolige, Giorgio Grisetti, Rainer Kümmerle, Wolfram Burgard, Benson Limketkai, and Regis Vincent. Efficient sparse pose adjustment for 2d mapping. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 22−29. IEEE, 2010.
    [20] 20 Michael Kaess, Ananth Ranganathan, and Frank Dellaert. isam: Incremental smoothing and mapping. IEEE Transactions on Robotics, 2008, 24(6): 1365−1378 doi: 10.1109/TRO.2008.2006706
    [21] Vadim Indelman, Stephen Williams, Michael Kaess, and Frank Dellaert. Factor graph based incremental smoothing in inertial navigation systems. In 2012 15th International Conference on Information Fusion, pages 2154−2161. IEEE, 2012.
    [22] 22 Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John J Leonard, and Frank Dellaert. isam2: Incremental smoothing and mapping using the bayes tree. The International Journal of Robotics Research, 2012, 31(2): 216−235 doi: 10.1177/0278364911430419
    [23] 23 Tong Qin, Peiliang Li, and Shaojie Shen. Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 2018, 34(4): 1004−1020 doi: 10.1109/TRO.2018.2853729
    [24] Tong Qin and Shaojie Shen. Online temporal calibration for monocular visual-inertial systems. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3662−3669. IEEE, 2018.
    [25] Shuaixin Li, Guangyun Li, Yanglin Zhou, Li Wang, and Jingyang Fu. Real-time dead reckoning and mapping approach based on threedimensional point cloud. In China Satellite Navigation Conference, pages 643−662. Springer, 2018.
    [26] Timothy D Barfoot. State Estimation for Robotics. Cambridge University Press, 2017.
    [27] 27 Ji Zhang and Sanjiv Singh. Laser-visual-inertial odometry and mapping with high robustness and low drift. Journal of Field Robotics, 2018, 35(8): 1242−1264 doi: 10.1002/rob.21809
    [28] Jens Behley and Cyrill Stachniss. Efficient surfel-based slam using 3d laser range data in urban environments. In Robotics: Science and Systems, 2018.
  • 加载中
计量
  • 文章访问数:  4734
  • HTML全文浏览量:  2798
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-06-02
  • 录用日期:  2019-12-15
  • 网络出版日期:  2020-01-16

目录

    /

    返回文章
    返回