期刊文献+

基于ORB-SLAM2的三维占据网格地图的实时构建 被引量:3

Real-time Construction of 3D Occupied Grid Map Based on ORB-SLAM2
下载PDF
导出
摘要 针对ORB-SLAM2系统只能输出相机的运动轨迹图,而不能生成用于路径规划和导航地图的问题,提出了一种基于ORB-SLAM2的跳表地图(SkipList Map)构建算法,可用于三维占据网格地图实时构建。首先搭建了一个用于三维占据网格地图实时构建的SkipList Map模型,其时间复杂度仅为O(lg n);其次对SkipList Map三维占据网格地图的生成与更新做了详细推导;最后设计了ORB-SLAM2与SkipList Map算法相结合的方案。通过效率对比实验,表明本文算法具有较高的时间效率与灵活性;搭建实验所需平台并进行了真实场景实时实验,实验表明本文算法能实现三维网格地图的实时构建;且能清晰标识出环境中障碍物的位置,验证了本文算法的有效性。 To overcome drawbacks of the ORB-SLAM2 system that can output only the motion trajectory map of camera but not generate maps for path planning and navigation,an algorithm based on ORB-SLAM2 for SkipList Map was proposed,with which three-dimensional occupation can be used.Firstly,a SkipList Map model for real-time construction of 3D occupied grid maps was built,and its time complexity was only O(lg n).Secondly,generation and update of SkipList Map 3D occupied grid maps were deduced in detail.Finally,a method combing ORB-SLAM2 and SkipList Map algorithm was designed.As shown in our efficiency comparison experiments,the proposed algorithm reached high temporal efficiency and good flexibility.Moreover,the platform needed for experiment was built and real-time experiment of real scene was carried out.The experiment shows that the algorithm could realize the real-time construction of 3D grid map and clearly identify the location of obstacles in the environment,from which the effectiveness of the proposed algorithm was proved.
作者 王飞 王耀力 WANG Fei;WANG Yao-li(College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China)
出处 《科学技术与工程》 北大核心 2020年第1期239-245,共7页 Science Technology and Engineering
基金 国家自然科学基金(61828601) 山西省自然科学基金(201801D121141)
关键词 ORB-SLAM2 网格地图模型 跳表地图 三维占据网格地图 ORB-SLAM2 grid map model SkipList map 3D occupied grid map
  • 相关文献

参考文献1

二级参考文献25

  • 1[1]Smith R, Self M, Chesseman P. Estimating uncertain spatial relationships in robotics[A]. Proceedings of Conference on Uncertainty in Artificial Intelligence[C]. Amsterdam: North-Holland, 1988. 435-461.
  • 2[2]Csorba M. Simultaneous Localization and Map Building[D]. Oxford: University of Oxford, 1997.
  • 3[3]Dissanayake G, Newman P M, et al. A solution to the simultaneous localization and map building (SLAM) problem[J]. IEEE Transactions on Robotics and Automation, 2001, 17(3): 229-241.
  • 4[4]Leonard J J, Durrant-Whyte F. Simultaneous map building and localization for an autonomous mobile robot[A]. Proceedings of the IEEE International workshop on Intelligent Robots and Systems[C]. Osaka, Japan: 1991. 1442-1447.
  • 5[5]Leonard J J, Feder H J S. A computationally efficient method for large-scale concurrent mapping and localization[A]. Proceedings of the Ninth International Symposium on Robotics Research[C]. London: Springer-Verlag, 1999. 316-321.
  • 6[6]Guivant J, Nebot E, Baiker S. Autonomous navigation and map building using laser range sensors in outdoor applications[J]. Journal of Robotic Systems, 2000, 17 (10): 565-583.
  • 7[7]Wan E, Merwe R. The unscented Kalman-filter for nonlinear estimation[A]. Proceedings of the IEEE Symposium on Adaptive Systems for Signal Processing[C]. Alberta, Canada: 2000. 153-158.
  • 8[8]Castellanos J A, Tardos J D, Schmidt G. Building a global map of the environment of a robot: the importance of correlations[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. 1997.1053-1059.
  • 9[9]Leonard J, Feder H J S. Decoupled stochastic mapping[J]. IEEE Journal of Oceanic Engineer, 2001,26(4): 561-571.
  • 10[10]Williams S B. Efficient Solutions to Autonomous Mapping and Navigation Problems[D]. Sydney: University of Sydney, 2001.

共引文献34

同被引文献17

引证文献3

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部