Obstacle region recognition using single image for planetary soft landing

YANG Shi-kun; XU Rui; ZHU Sheng-ying

doi:10.3788/OPE.20172513.0318

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Obstacle region recognition using single image for planetary soft landing

更新时间：2020-08-13

- Obstacle region recognition using single image for planetary soft landing
- Optics and Precision Engineering Vol. 25, Issue 10s, Pages: 318-324(2017)
- 作者机构：
  
  1. 北京理工大学深空探测技术研究所北京,100081
  2. “深空自主导航与控制”工信部重点实验室, 北京 100081
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172513.0318
  CLC： TP394.41
- Received：10 July 2017，
  
  Revised：27 July 2017，
  
  Published：25 November 2017
- 稿件说明：
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杨世坤, 徐瑞, 朱圣英. 单幅图像行星软着陆障碍区识别[J]. 光学精密工程, 2017,25(10s): 318-324

YANG Shi-kun, XU Rui, ZHU Sheng-ying. Obstacle region recognition using single image for planetary soft landing[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 318-324
杨世坤, 徐瑞, 朱圣英. 单幅图像行星软着陆障碍区识别[J]. 光学精密工程, 2017,25(10s): 318-324 DOI： 10.3788/OPE.20172513.0318.

YANG Shi-kun, XU Rui, ZHU Sheng-ying. Obstacle region recognition using single image for planetary soft landing[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 318-324 DOI： 10.3788/OPE.20172513.0318.

摘要

为了解决复杂地形情况下的探测器软着陆障碍检测问题，本文提出了一种基于单幅图像的被动视觉障碍区识别方法。首先，使用基于相位谱法的显著性检测获取图像的显著图，并计算原图像的灰度方差图。接着，以原图像每个像素点对应的显著图像素灰度及方差图像素灰度为两特征量，构建地形图像的二维灰度直方图。通过设计分段伽玛校正函数对两幅特征图像进行增强，使二维灰度直方图呈现良好的峰值特性。然后采用二维大津法对灰度直方图进行分割，得到障碍区域的二值图像。最后使用HIRISE提供的火星地形灰度图像以及对应的高程数据，对障碍区识别算法进行评价。实验表明，算法平均识别正确率TN+TP达到80%，能够在复杂地形环境下有效地分割出障碍区域以及安全区域，为着陆点选择提供参考。

Abstract

In order to overcome the problem of obstacle detection in complex terrain for planet soft landing

a passive visual obstacle detection method based on single image was proposed. First

a saliency map of the image was obtained using the saliency detection based on the phase spectrum method

and the variance map of the original image was calculated. Then

the two-dimensional histogram of terrain image was constructed by counting the gray level of two graphs

and the segmented Gamma correction was used to enhance the peak feature. Furthermore the two-dimensional histogram of terrain image was segmented by the two-dimensional Otsu method

which was used to obtain the binary image of the obstacle area. Herein

the various terrain grayscale images provided by HIRISE and the corresponding DEM data were employed to evaluate the accuracy of the obstacle detection algorithm. The average detection rate of TN + TP is over 80%. Experiment result indicates that the algorithm can effectively segment the obstacle area and the safety area in complex terrain environments

thus providing useful information for landing point selection.

关键词

Keywords

references

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