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基于视差平面分割的移动机器人障碍物地图构建方法 被引量：2

Disparity Image Plane Segmentation Based Obstacle Map Construction for Mobile Robot
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摘要 作为自主移动机器人地表障碍物探测(GPOD)技术的一部分,提出了一种利用双目摄像机的视差图像获取信息来构建机器人前方障碍物栅格地图的方法.该方法融合了3维立体视觉技术以及2维图像处理技术,前者依据视差图的直方图信息对视差图像进行自适应平面分割,把每个平面看作是3维场景中的实物切片进而提取障碍物3维信息,后者通过计算各平面上的障碍物信息曲线来提取障碍物信息,把立体视觉数据从视差图像空间变换到2维的障碍物地图空间.给出了该方法构建障碍物地图的整体过程,试验结果证明了该算法的有效性和精确性. As a part of GPOD （ground plane obstacle detection） technology of autonomous mobile robot, a method for creating front obstacle grid map is presented which utilizes disparity image of binocular camera to obtain information. This method combines both the stereo vision and the traditional 2D image processing technique. The former implements autonomous plane segmentation based on the histogram of disparity image, and regards each plane as scene slice of 3D scenario to extract obstacle＇s 3D information. The later extracts obstacle information by calculating obstacle profile for each plane, and transforms stereo data from disparity image space to 2D obstacle map space. The obstacle map construction in this method is presented. The experiment results prove the validity and accuracy of this method.
作者 宋鑫坤 陈万米 徐昱琳 张雷
机构地区 上海大学机电工程与自动化学院 上海市电站自动化技术重点实验室
出处 《机器人》 EI CSCD 北大核心 2010年第2期171-178,共8页 Robot
基金 上海市重点学科建设项目(T0103)
关键词 视差图像 平面分割 障碍物地图 GPOD disparity image plane segmentation obstacle map GPOD （ground plane obstacle detection）
分类号 TP242.6 [自动化与计算机技术—检测技术与自动化装置]
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参考文献16

  • 1Van der Mark W, van den Heuvel J C, Groen F C A. Stereo based obstacle detection with uncertainty in rough terrain[C]//IEEE Intelligent Vehicles Symposium. Piscataway, NJ, USA: IEEE, 2007: 1005-1012.
  • 2Suganuma N, Fujiwara N. Obstacle map generation using virtual disparity image[C]//SICE Annual Conference. Tokyo, Japan: SICE, 2007: 1523-1528.
  • 3Broggi A. Robust real-time lane and road detection in critical shadow condition[C]//IEEE International Conference on Computer Vision. Piscataway, NJ, USA: IEEE, 1995:353-358.
  • 4Badal S, Ravela S, Draper B, et al. A practical obstacle detection and avoidance system[C]//IEEE Workshop on Applications of Computer Vision. Piscataway, NJ, USA: IEEE, 1994: 97-104.
  • 5Agrawal M, Konolige K. Real-time localization in outdoor environments using stereo vision and inexpensive GPS[C]//International Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 2006: 1063-1068.
  • 6Kolmogorov V, Criminisi A, Blake A, et al. Bi-layer segmentation of binocular stereo video[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA, USA: IEEE Computer Society, 2005: 407-414.
  • 7Blake A. Stereo vision and segmentation[C]//IEEE Conference on Advanced Video and Signal Based Surveillance. Piscataway, NJ, USA: IEEE, 2007: 4-7.
  • 8Bostelman R, Hong T, Madhavan R, et al. Performance analysis of an autonomous mobile robot mapping system for outdoor environments[C]//lntemational Conference on Control, Automation, Robotics and Vision. Piscataway, NJ, USA: IEEE, 2006: 1249-1254.
  • 9Krueger F A. An algorithm for automatically mapping an arbitrary 2D environment[C]//IEEE International Conference on Systems, Man and Cybernetics. Piscataway, NJ, USA: IEEE, 2004: 958-963.
  • 10Ferrari F, Grosso E, Sandini G, et al. A stereo vision system for real time obstacle avoidance in unknown environment[C]//IEEE International Workshop on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 1990: 703-708.

同被引文献22

  • 1Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J]. International Journal of Computer Vision, 2002, 47(1/2/3): 7-42.
  • 2Yang Q Q, Li D X, Wang L H, et al. Full-image guided filter- ing for fast stereo matching[J]. IEEE Signal Processing Letters, 2013, 20(3): 237-240.
  • 3Xiong W, Chung H S, Jia J Y. Fractional stereo matching us- ing expectation-maximization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(3): 428-443.
  • 4Zitnik C L, Kanade T A. Cooperative algorithm for stereo matching and occlusion detection[J]. IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 2000, 22(7): 675-684.
  • 5Finlayson G, Xu R. Illuminant and gamma comprehensive nor- malisation in log RGB space[J]. Pattern Recognition Letters, 2003, 24(11): 1679-1690.
  • 6Finlayson G D, Hordley S D, Hubel P M. Color by corre- lation: A simple, unifying framework for color constancy[J]. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2011, 23(11): 1209-1221.
  • 7Hirschmuller H, Scharstein D. Evaluation of stereo matching costs on images with radiometric differences[J]. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 2009, 31(9): 1582-1599.
  • 8Heo Y S, Lee K M, Lee S U. Robust stereo matching using adaptive normalized cross-correlation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(4): 807- 822.
  • 9Heo Y S, Lee K M, Lee S U. Joint depth map and color consis- tency estimation for stereo images with different illuminations and cameras[J]. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 2013, 35(5): 1094-1106.
  • 10Yang Q X, Ahuja N. Stereo matching using epipolar distance transform[J]. IEEE Transactions on Image Processing, 2012, 21(10): 4410-4419.

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机器人
2010年 第2期

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