Robot vision system for 3D reconstruction in low texture environment

doi:10.3788/OPE.20152302.0540

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Robot vision system for 3D reconstruction in low texture environment

更新时间：2020-08-13

- Robot vision system for 3D reconstruction in low texture environment
- Optics and Precision Engineering Vol. 23, Issue 2, Pages: 540-549(2015)
- 作者机构：
  
  1. 北京邮电大学信息光子学与光通信研究院北京,100876
  2. 北京信息科技大学光电测试技术北京市重点实验室北京,100192
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152302.0540
  CLC： TP242.6;TP391.4
- Received：17 July 2014，
  
  Revised：11 September 2014，
  
  Published：25 February 2015
- 稿件说明：
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林义闽, 吕乃光, 娄小平等. 用于弱纹理场景三维重建的机器人视觉系统[J]. 光学精密工程, 2015,23(2): 540-549

LIN Yi-min, L&#220, Nai-guang etc. Robot vision system for 3D reconstruction in low texture environment[J]. Editorial Office of Optics and Precision Engineering, 2015,23(2): 540-549
林义闽, 吕乃光, 娄小平等. 用于弱纹理场景三维重建的机器人视觉系统[J]. 光学精密工程, 2015,23(2): 540-549 DOI： 10.3788/OPE.20152302.0540.

LIN Yi-min, L&#220, Nai-guang etc. Robot vision system for 3D reconstruction in low texture environment[J]. Editorial Office of Optics and Precision Engineering, 2015,23(2): 540-549 DOI： 10.3788/OPE.20152302.0540.

摘要

为了实现机器人在弱纹理场景中的避障和自主导航

构建了由双目相机和激光投点器构成的主动式双目视觉系统。对立体视觉密集匹配问题进行了研究:采用激光投点器投射出唯一性和抗噪性较好的光斑图案

以增加场景的纹理信息;然后

基于积分灰度方差(IGSV)和积分梯度方差(IGV)提出了自适应窗口立体匹配算法。该算法首先计算左相机的积分图像

根据积分方差的大小确定匹配窗口内的图像纹理质量

然后对超过预设方差的阈值与右相机进行相关计算

最后通过遍历整幅图像得到密集的视差图。实验结果表明:该视觉系统能够准确地恢复出机器人周围致密的3D场景

3D重建精度达到0.16 mm

满足机器人避障和自主导航所需的精度。与传统的算法相比

该匹配方法的图像方差计算量不会随着窗口尺寸的增大而增加

从而将密集匹配的运算时间缩短了至少93%。

Abstract

To realize the obstacle avoidance and automatic navigation of a robot in a low texture environment

an active stereo visual system consisting a binocular camera and a compact laser projector was established. The dense stereo matching algorithm was investigated. Firstly

the compact laser projector generated the spot patterns with excellent uniqueness and anti-noise performance for increasing the texture information. Then

an adaptive-window matching algorithm was proposed based on Integral Grayscale Variance(IGSV)and Integral Gradient Variance(IGV). The algorithm was used calculate the integral variance in a matching window using the integral image obtained by the left image. If it was greater than the variance threshold

the correlation between the left and right image pixels was calculated to get the dense disparity maps. Experimental results show that the vision system accurately gets the 3D dense scene around the robot and the 3D reconstruction accuracy is 0.16 mm

which is suitable for the obstacle avoidance and automatic navigation. As compared with the traditional methods

the computation cost of dense matching has at least decreased by 93% since the computation used for image variance could not increase with the size of the matching window.

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