基于双目视觉的微型无人机室内3维地图构建

余小欢, 韩波, 张宇, 李平

余小欢, 韩波, 张宇, 李平. 基于双目视觉的微型无人机室内3维地图构建[J]. 信息与控制, 2014, 43(4): 392-397. DOI: 10.13976/j.cnki.xk.2014.0392
引用本文: 余小欢, 韩波, 张宇, 李平. 基于双目视觉的微型无人机室内3维地图构建[J]. 信息与控制, 2014, 43(4): 392-397. DOI: 10.13976/j.cnki.xk.2014.0392
YU Xiaohuan, HAN Bo, ZHANG Yu, LI Ping. Binocular Stereo Vision Based 3D Mapping for Micro Aerial Vehicles in an Indoor Environment[J]. INFORMATION AND CONTROL, 2014, 43(4): 392-397. DOI: 10.13976/j.cnki.xk.2014.0392
Citation: YU Xiaohuan, HAN Bo, ZHANG Yu, LI Ping. Binocular Stereo Vision Based 3D Mapping for Micro Aerial Vehicles in an Indoor Environment[J]. INFORMATION AND CONTROL, 2014, 43(4): 392-397. DOI: 10.13976/j.cnki.xk.2014.0392
余小欢, 韩波, 张宇, 李平. 基于双目视觉的微型无人机室内3维地图构建[J]. 信息与控制, 2014, 43(4): 392-397. CSTR: 32166.14.xk.2014.0392
引用本文: 余小欢, 韩波, 张宇, 李平. 基于双目视觉的微型无人机室内3维地图构建[J]. 信息与控制, 2014, 43(4): 392-397. CSTR: 32166.14.xk.2014.0392
YU Xiaohuan, HAN Bo, ZHANG Yu, LI Ping. Binocular Stereo Vision Based 3D Mapping for Micro Aerial Vehicles in an Indoor Environment[J]. INFORMATION AND CONTROL, 2014, 43(4): 392-397. CSTR: 32166.14.xk.2014.0392
Citation: YU Xiaohuan, HAN Bo, ZHANG Yu, LI Ping. Binocular Stereo Vision Based 3D Mapping for Micro Aerial Vehicles in an Indoor Environment[J]. INFORMATION AND CONTROL, 2014, 43(4): 392-397. CSTR: 32166.14.xk.2014.0392

基于双目视觉的微型无人机室内3维地图构建

基金项目: 国家自然科学基金资助项目(61005085);中央高校基本科研业务费专项资金资助(2012QNA4024)
详细信息
    作者简介:

    余小欢(1987-),男,硕士生.研究领域为无人机的视觉导航,嵌入式系统.
    韩波(1969-),男,博士,副教授.研究领域为计算机控制,嵌入式系统,空中机器人控制与导航.
    张宇(1980-),男,博士,讲师.研究领域为人工智能,视觉导航,无人机导航、制导与控制.

    通讯作者:

    韩波,bhan@iipc.zju.edu.cn

  • 中图分类号: TP391.41

Binocular Stereo Vision Based 3D Mapping for Micro Aerial Vehicles in an Indoor Environment

  • 摘要: 针对微型无人机的室内避障和路径规划的需求,搭建了一个基于BeagleBoard-xM板和消费级别摄像头的低成本嵌入式双目视觉硬件平台.通过双目视觉获取微型无人机所在的室内环境的3维(3D)点云描述,基于8叉树结构的3维空间描述模型和反向传感器模型,结合无人机的姿态信息,提出了基于双目视觉的3D占有率栅格图描述的室内环境的3维地图构建方法.实验结果表明,基于嵌入式双目视觉平台获取到的3D占有率栅格地图准确、 有效地描述了微型无人机当前的室内3维环境信息,可以广泛地应用于无人机的室内导航.
    Abstract: In order to meet the demands of obstacle avoidance and path planning for micro aerial vehicle (MAV) in an indoor environment,we establish a low cost embedded binocular stereo vision platform based on a BeagleBoard-xM board and consumer-grade cameras. With the indoor environment information obtained through the binocular stereo vision system,based on a 3D space description model-octree and an inverse sensor model and combined with the attitude information of the MAV,we propose a 3D map-building method described with a 3D occupancy map. The result of the experiment shows that the 3D occupancy map acquired by embedded binocular stereo vision system described the indoor environment of MAV accurately and effectively. It can thus be used widely in unmanned aerial vehicle navigation in an indoor environment.
  • [1] Achtelik M,Bachrach A,He R,et al. Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments[C]//SPIE Conference on Unmanned Systems Technology XI. Orlando,Florida,USA: SPIE,2009: 733219-10.
    [2] Bachrach A,He R,Roy N. Autonomous flight in unknown indoor environments[J]. International Journal of Micro Air Vehicles,2009,1(4): 217-228.
    [3] Mason J,Ricco S,Parr R. Textured occupancy grids for monocular localization without features[C]//IEEE International Conference on Robotics and Automation. Piscataway,NJ,USA: IEEE,2011: 5800-5806.
    [4] Mirisola L G B,Lobo J,Dias J. 3D map registration using vision/laser and inertial sensing[C]//European Conference on Mobile Robots. 2007.
    [5] Hrabar S. Reactive obstacle avoidance for rotorcraft UAVs[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway,NJ,USA: IEEE,2011: 4967-4674.
    [6] Bradski G,Kaebler A. Learning OpenCV[M]. Sebastopol,USA: O'Reilly Media,Inc.,2008.
    [7] Kitt B,Geiger A,Lategahn H. Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme[C]//2010 IEEE Intelligent Vehicles Symposium (IV). Piscataway,NJ,USA: IEEE,2010: 486-492.
    [8] Lobo J,Dias J. Relative pose calibration between visual and inertial sensors[J]. International Journal of Robotics Research,2007,26(6): 561-575.
    [9] Bouguet J Y. Pyramidal implementation of the affine Lucas Kanade feature tracker description of the algorithm[R]. Santa Clara,CA,USA: Intel Corporation,2001.
    [10] 杜歆. 用于导航的立体视觉系统[D]. 杭州: 浙江大学,2003.
    Du X. Stereo vision used for navigation[D]. Hangzhou: Zhejiang University,2003.
    [11] Morris W,Dryanovski I,Xiao J. 3D indoor mapping for micro-UAVs using hybrid range finders and multi-volume occupancy grids[C]//RSS 2010 Workshop on RGB-D: Advanced Reasoning with Depth Cameras. 2010.
    [12] Hornung A,Wurm K M,Bennewitz M,et al. OctoMap: An efficient probabilistic 3D mapping framework based on octrees[J]. Autonomous Robots,2013,34(3): 189-206.
    [13] Hrabar S. 3D path planning and stereo-based obstacle avoidence for rotorcraft UAVs[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway,NJ,USA: IEEE,2008: 807-814.
    [14] Heng L,Meier L,Tanskanen P,et al. Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing[C]//IEEE International Conference on Robotics and Automation. Piscataway,NJ,USA: IEEE,2011: 2472-2477.
    [15] Ferguson D,Stentz A. Field D*: An interpolation-based path planner and replanner [M]//Robotics Research. Berlin,Germany: Springer-Verlag,2007: 239-253.
    [16] Goldberg S B,Matthies L. Stereo and IMU assisted visual odometry on an OMAP3530 for small robots [C]//2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Piscataway,NJ,USA: IEEE,2011: 169-176.
    [17] Izadi S,Kim D,Hilliges O,et al. KinectFusion: Real-time 3D reconstruction and interaction using a moving depth camera[C]//Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology. New York,NJ,USA: ACM,2011: 559-568.
计量
  • 文章访问数:  1741
  • HTML全文浏览量:  9
  • PDF下载量:  1112
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-07-29
  • 发布日期:  2014-08-19

目录

    /

    返回文章
    返回
    x