Removing dust impact for visual navigation in Mars landing

Abstract Visual navigation has received more and more attention in Mars landing. However, dust devils are active on Mars. The dust will make a great influence on visual navigation during the landing phase. In this paper, a simple but effective approach was proposed to remove the dust impact for visual navigation in Mars landing. This method was based on a model which was widely used to describe the scene radiance that was affected by different weather conditions. First the calculation method of transmission parameter was deduced from this model. Then the value of the global atmospheric light was estimated through the detection of most dust-opaque region. After all unknown variables were determined, the clear image was recovered by the corresponding formula and calculation method. For it is difficult to obtain the decent images that appear while the Mars rover enters the landing phase, a simulated dust environment was created in the lab and some images affected by dust were obtained to check the validity of this method. From the results of the experiments, the proposed approach can effectively eliminate the dust influences and provide clearer pictures. The clear images help to provide more precise data for visual navigation.

[1]  Sun Xian-kun.  Color image enhancement algorithm based on human visual system , 2009 .

[2]  Raanan Fattal.  Single image dehazing , 2008, SIGGRAPH 2008.

[3]  Xing Kui Wang,et al.  The Dedusting Method Based on a Single Still Image , 2013 .

[4]  R. Hanel,et al.  Mariner 9 michelson interferometer. , 1972, Applied optics.

[5]  Matthew P. Larkin,et al.  A Simple Thermodynamical Theory for Dust Devils , 1998 .

[6]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[7]  Shree K. Nayar,et al.  Chromatic framework for vision in bad weather , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[8]  Wei Tang,et al.  Single Remote Sensing Image Dehazing , 2014, IEEE Geoscience and Remote Sensing Letters.

[9]  Yuanzong Li,et al.  The method of image restoration in the environments of dust , 2010, 2010 IEEE International Conference on Mechatronics and Automation.

[10]  Carl Sagan,et al.  Physical properties of the particles composing the Martian dust storm of 1971–1972 , 1977 .

[11]  Yasuhiro Morita,et al.  Autonomous Precision Landing System with Avoidance System using Single Camera for Small Landers , 2013 .

[12]  C. Prabhakara,et al.  Infrared Spectroscopy Experiment on the Mariner 9 Mission: Preliminary Results , 1972, Science.

[13]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[14]  Robby T. Tan,et al.  Visibility in bad weather from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  R. Anderson,et al.  Mars Science Laboratory Mission and Science Investigation , 2012 .

[16]  Erwin Mooij,et al.  Stereo Vision Algorithm for Hazard Detection during Planetary Landings , 2014 .

[17]  P. Gierasch,et al.  Dust Devils on Mars , 1985, Science.

[18]  P. Pina,et al.  Automated determination of the orientation of dust devil tracks in mars orbiter images , 2014 .

[19]  Robert N. Ingoldby.  Guidance and Control System Design of the Viking Planetary Lander , 1977 .

[20]  Hutao Cui,et al.  A new approach based on crater detection and matching for visual navigation in planetary landing , 2014 .

[21]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  R. A. Hanel,et al.  Investigation of the Martian environment by infrared spectroscopy on Mariner 9 , 1972 .