GNSS World of China

Volume 47 Issue 5
Nov.  2022
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GAO Yi, WANG Qing, YANG Gaochao, LIU Pengfei. Indoor dynamic SLAM based on geometric constraints and target detection[J]. GNSS World of China, 2022, 47(5): 51-56. doi: 10.12265/j.gnss.2022099
Citation: GAO Yi, WANG Qing, YANG Gaochao, LIU Pengfei. Indoor dynamic SLAM based on geometric constraints and target detection[J]. GNSS World of China, 2022, 47(5): 51-56. doi: 10.12265/j.gnss.2022099
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Indoor dynamic SLAM based on geometric constraints and target detection

doi: 10.12265/j.gnss.2022099

  • College of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

  • Received Date: 2022-06-06
  • Accepted Date: 2022-06-06
    • Available Online: 2022-10-28
    Abstract
  • Aiming at the problem of low localization accuracy and poor map effect of visual simultaneous localization and mapping (SLAM) in indoor dynamic environment, a indoor dynamic SLAM method is proposed based on geometric constraints and target detection. The target detection network is used to obtain semantic information and a method for missing detection of moving objects is proposed. Based on prior knowledge, an information determination method is proposed to accurately identify dynamic regions. Dynamic points are eliminated based on geometric constraints and deep learning. Static points are used to estimate camera pose. A closed-loop static map is builded based on the stored information. The experiment on TUM dataset shows that the localization accuracy is 97.5% higher than that of ORB-SLAM2 and the performance is better than other dynamic SLAM. The experiment in the indoor real environment shows that the static map is more accurate. The localization accuracy and the map effect of indoor dynamic SLAM are improved effectively.

     

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    • References(18)
  • [1]
    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. DOI: 10.1109/TRO.2016.2624754
    [2]
    JIA Y J, YAN X Y, XU Y H. A survey of simultaneous localization and mapping for robot[C]// IEEE Advanced Information Technology, Electronic and Automation Control Conference, 2019.
    [3]
    FORSTER C, ZHANG Z C, GASSNER M, et al. SVO: semidirect visual odometry for monocular and multicamera systems[J]. IEEE transactions on robotics, 2016, 33(2): 249-265. DOI: 10.1109/TRO.2016.2623335
    [4]
    ENGEL J, KOLTUN V, CREMERS D. Direct sparse odometry[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(3): 611-625. DOI: 10.1109/TPAMI.2017.2658577
    [5]
    MUR-ARTAL R, TARDÓS J D. Orb-slam2: an open-source slam system for monocular, stereo, and RGB-D cameras[J]. IEEE transactions on robotics, 2017, 33(5): 1255-1262. DOI: 10.1109/TRO.2017.2705103
    [6]
    CAMPOS C, ELVIRA R, RODRÍGUEZ J J G, et al. Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap SLAM[J]. IEEE transactions on robotics, 2021, 37(6): 1874-1890. DOI: 10.1109/TRO.2021.3075644
    [7]
    王柯赛, 姚锡凡, 黄宇, 等. 动态环境下的视觉SLAM研究评述[J]. 机器人, 2021, 43(6): 715-732. DOI: 10.13973/j.cnki.robot.200468
    [8]
    高兴波, 史旭华, 葛群峰, 等. 面向动态物体场景的视觉SLAM综述[J]. 机器人, 2021, 43(6): 733-750. DOI: 10.13973/j.cnki.robot.200323
    [9]
    SUN Y X, LIU M, MENG M Q H. Improving RGB-D SLAM in dynamic environments: a motion removal approach[J]. Robotics and autonomous systems, 2017(89): 110-122. DOI: 10.1016/j.robot.2016.11.012
    [10]
    张慧娟, 方灶军, 杨桂林. 动态环境下基于线特征的RGB-D视觉里程计[J]. 机器人, 2019, 41(1): 75-82. DOI: 10.13973/j.cnki.robot.180020
    [11]
    SHENG C, PAN S G, GAO W, et al. Dynamic-DSO: direct sparse odometry using objects semantic information for dynamic environments[J]. Applied sciences, 2020, 10(4): 1467. DOI: 10.3390/app10041467
    [12]
    HE K, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C] //The IEEE International Conference on Computer Vision, 2017: 2961-2969.
    [13]
    YU C, LIU Z X, LIU X J, et al. DS-SLAM: a semantic visual SLAM towards dynamic environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018: 1168-1174. DOI:10.1109/IROS.2018.8593691
    [14]
    BADRINARAYANAN V, KENDALL A, CIPOLLA R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(12): 2481-2495. DOI: 10.1109/TPAMI.2016.2644615
    [15]
    YANG S Q, FAN G H, BAI L L, et al. SGC-VSLAM: a semantic and geometric constraints VSLAM for dynamic indoor environments[J]. Sensors, 2020, 20(8): 2432. DOI: 10.3390/s20082432
    [16]
    REDMON J, FARHADI A. Yolov3: an incremental improvement[J]. arXiv e-print, 2018. DOI: 10.48550/arXiv.1804.02767
    [17]
    ZHONG F W, WANG S, ZHANG Z Q, et al. Detect-SLAM: making object detection and SLAM mutually beneficial[C]//IEEE Winter Conference on Applications of Computer Vision (WACV), 2018: 1001-1010. DOI: 10.1109/WACV.2018.00115
    [18]
    DELMERICO J, SCARAMUZZA D. A benchmark comparison of monocular visual-inertial odometry algorithms for flying robots[C]//IEEE International Conference on Robotics and Automation (ICRA), 2018: 2502-2509. DOI:10.1109/ICRA.2018.8460664
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