车辆转弯中道路前方障碍位置的自动检测.doc
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车辆转弯中道路前方障碍位置的自动检测,摘要障碍物检测在智腀@盗臼泳醯己较低持姓加惺种匾淖饔谩；诨魇泳跫际醵哉习锏募觳夤桃治礁龇矫妫赫习锏氖侗鸷驼习锞嗬氲牟馑恪Ｂ畚氖紫冉樯芰酥悄@@盗驹诠谕獾姆⒄骨榭觯潭致哿嘶诘ツ渴泳醮淼恼习锸侗鸺际酢Ｗ酆戏治隽烁髦滞枷穹指罘椒ǎ⑼ü笛槎员确治觯∮盟宸ㄇ笕°兄岛螅酝枷窠写恚⒏...
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ABSTRACT
Obstacle detection in the intelligent vehicle vision navigation system occupies a very important role£®Obstacle detection process is divided into two aspects: the identification of obstacles and obstructions distance estimates based on machine vision technology.
At the beginning of this paper, we firstly introduce the development of intelligence automobile oversea and domestic. Followed by, discussed the deal with the obstacle recognition technology based on monocular vision. Comprehensive analysis of a variety of image segmentation method, and experimental comparison, and selection of the bimodal France to strike the threshold, image processing, and to identify the obstacles according to the image's pixel difference. Least squares fit to the location of the obstacle in the image.
Prove the paper presents the obstacle detection and recognition technology to distinguish between pseudo-obstruction preliminary experimental comparison groups. And can estimate the location of the obstacle in the image. The experimental results show that the method has a certain timeliness, reliability and accuracy.
Key words: Monocular Vision; Image processing; Monocular Measurement; Obstacle Detection
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[1]Àî±ó, ÍõÈÙ±¾, ¹ù¿ËÓÑ. »ùÓÚ»úÆ÷ÊÓ¾õµÄÖÇÄ@@µÁ¾ÕÏ°Îï¼ì²â·½·¨Ñо¿[J]. ¹«Â·½»Í¨¿Æ¼¼,2002Äê8Ô ,Vo1.19 No14
[2]¹Ë°ØÔ°£¬ÍõÈÙ±¾£¬ÓàÌìºéµÈ£®»ùÓÚÊÓ¾õµÄÇ°·½³µÁ¾Ì½²â¼¼ÊõÑо¿·½·¨×ÛÊö[J]£®¹«Â·½»Í¨¿Æ¼¼£¬2005£¬22(10)£ºl14-120£®
κâù, ÀîÓî, ºúÊæÃÃ. ÃæÏò¸´ÔÓ±³¾°ÖвÊÉ«Á¢ÌåͼÏñµÄÕÏ°Îï¼ì²â[J]. ×Ô¶¯»¯¼¼ÊõÓëÓ¦ÓÃ[J].2007Äê, Vo1.26 No 7
[3]C.Thorpe, J.D.Carlson, D.Duggins, ets. Safe Robot Driving in Clucttered Environments. Proceedings of the 11th International Symposium of Robotics Research, October, 2003.
[4]P£®Bellutta, R£®Manduchi£¬L Matthles£®Terrain Peree Ption for DEMOl l l[C]£®Proceedings of the IEEE Intel ligent Vehicles SynlPoslum£¬2000£¬326-331£®
[5]Alberto Broggi£¬Massimo Bertozzl£¬Alessandra Faseiol i£®Automatic Vehicle Guidance£ºthe Experience of the ARGO Autonomous Vehicles [M]£¬USA£®World Science Publishing Co. Ltd. 1999£®
[6]Massimo Bertozzi£¬Alberto Broggi£®GOLD£ºA parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection[C]£®IEEE Transactions on Image Proeessing, 1998,7(1)£º62-81£®
[7]ÕÅÅó·É£¬ºÎ¿ËÖÒ£¬Å·ÑôÕýÖùµÈ£®¶à¹¦ÄÜÊÒÍâÖÇÄÜÒƶ¯»úÆ÷ÈËÊÔÑéƽ̨-THMR-V[J]£¬2002£¬24(2)£º97-101£®
[8]´ÞÐÇ£¬ÈòÇ嶫£®»ùÓÚÖ¡¼ä²î·Ö·½·¨µÄµÀ·³µÁ¾¼ì²âϵͳ[J]£®Î¢¼ÆËã»úÐÅÏ¢£¬2007£¬23(4-1)£®
[9]Ñîºé·â£®ÊÓƵÐòÁÐÖÐÔ˶¯Ä¿±êµÄʵʱ·Ö¸îÓë¸ú×Ù£®[˶ʿѧλÂÛÎÄ]£®Î÷°²£ºÎ÷°²Àí¹¤´óѧ£®2003£®3£º19-21
[10]GANDHIT£¬DEVADIGAS£¬KASTURIR£¬eta1£®Detection of Obstacles on Runways using Ego-motion Compensation and Tracking of Significant Features[J]Int£®J£®of Image and Vi sion Computing£¬2000£¬18(10)£º302-303£®
[11]ÑîÎĽܣ¬ºúÃ÷Î⣬Ñî¾²ÓһÖÖ»ùÓÚ¹âÁ÷µÄÕÏ°Îï¹À¼ÆËã·¨[J]£®¼ÆËã»ú¹¤³ÌÓëÓ¦Óã¬2006£¬42(5)£º80-81£®
[12]ÖìÔÆ·¼£¬ÍõêÝÊõ£¬¶Å짣®¾²Ì¬»·¾³ÖлùÓÚ¹âÁ÷µÄÕÏ°Îï¼ì²â[J]£®Õã½´óѧѧ±¨£¬2008£¬42(6)£®
[13]´Þ¸ß½¨£¬»ÆÒø»¨£¬ÌïÔ£¬»ùÓÚÁ¢ÌåÊÓ¾õµÄÇ°·½³µÁ¾Ì½²â[J]£®2005£¬13(9)£º890-899£®
[14]Ze hang Sun¡£George Bebis£¬Ronald Miller£®On-Road Vehicle Detection Using Evolutionary Gabor Filter Optimization. IEEE Transactions on Intell igent Transportation Systems£¬2005£¬6(2)£º125-137£®
[15]ÕÔÒ»±ø£¬ÍõÈÙ±¾£¬ÀîÁջԵȣ®»ùÓÚ¼¤¹âÀ×´ïµÄÎÞÈ˼ÝÊ»³µÇ°·½ÕÏ°Îï¼ì²â[J]£®½»Í¨Óë¼ÆËã»ú£¬2007£¬25(135)£®
[16]WANG Rongben. GU Bai yuan£¬JIN Li sheng£®Study on curb detection method based on 3D range image by laser radar£®Proceedings of the IEEE Intelligent Vehicles Symposium£¬USA£®2005£®
[17]ºòµÂÔ壬Àî¿ËÇ¿¡£ÐÂÐͳµÔØ̽²âÀ×´ïϵͳ¼¼ÊõÑо¿£®Öйú»úе¹¤³Ì£¬2004£¬15(21)..
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ABSTRACT
Obstacle detection in the intelligent vehicle vision navigation system occupies a very important role£®Obstacle detection process is divided into two aspects: the identification of obstacles and obstructions distance estimates based on machine vision technology.
At the beginning of this paper, we firstly introduce the development of intelligence automobile oversea and domestic. Followed by, discussed the deal with the obstacle recognition technology based on monocular vision. Comprehensive analysis of a variety of image segmentation method, and experimental comparison, and selection of the bimodal France to strike the threshold, image processing, and to identify the obstacles according to the image's pixel difference. Least squares fit to the location of the obstacle in the image.
Prove the paper presents the obstacle detection and recognition technology to distinguish between pseudo-obstruction preliminary experimental comparison groups. And can estimate the location of the obstacle in the image. The experimental results show that the method has a certain timeliness, reliability and accuracy.
Key words: Monocular Vision; Image processing; Monocular Measurement; Obstacle Detection
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[1]Àî±ó, ÍõÈÙ±¾, ¹ù¿ËÓÑ. »ùÓÚ»úÆ÷ÊÓ¾õµÄÖÇÄ@@µÁ¾ÕÏ°Îï¼ì²â·½·¨Ñо¿[J]. ¹«Â·½»Í¨¿Æ¼¼,2002Äê8Ô ,Vo1.19 No14
[2]¹Ë°ØÔ°£¬ÍõÈÙ±¾£¬ÓàÌìºéµÈ£®»ùÓÚÊÓ¾õµÄÇ°·½³µÁ¾Ì½²â¼¼ÊõÑо¿·½·¨×ÛÊö[J]£®¹«Â·½»Í¨¿Æ¼¼£¬2005£¬22(10)£ºl14-120£®
κâù, ÀîÓî, ºúÊæÃÃ. ÃæÏò¸´ÔÓ±³¾°ÖвÊÉ«Á¢ÌåͼÏñµÄÕÏ°Îï¼ì²â[J]. ×Ô¶¯»¯¼¼ÊõÓëÓ¦ÓÃ[J].2007Äê, Vo1.26 No 7
[3]C.Thorpe, J.D.Carlson, D.Duggins, ets. Safe Robot Driving in Clucttered Environments. Proceedings of the 11th International Symposium of Robotics Research, October, 2003.
[4]P£®Bellutta, R£®Manduchi£¬L Matthles£®Terrain Peree Ption for DEMOl l l[C]£®Proceedings of the IEEE Intel ligent Vehicles SynlPoslum£¬2000£¬326-331£®
[5]Alberto Broggi£¬Massimo Bertozzl£¬Alessandra Faseiol i£®Automatic Vehicle Guidance£ºthe Experience of the ARGO Autonomous Vehicles [M]£¬USA£®World Science Publishing Co. Ltd. 1999£®
[6]Massimo Bertozzi£¬Alberto Broggi£®GOLD£ºA parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection[C]£®IEEE Transactions on Image Proeessing, 1998,7(1)£º62-81£®
[7]ÕÅÅó·É£¬ºÎ¿ËÖÒ£¬Å·ÑôÕýÖùµÈ£®¶à¹¦ÄÜÊÒÍâÖÇÄÜÒƶ¯»úÆ÷ÈËÊÔÑéƽ̨-THMR-V[J]£¬2002£¬24(2)£º97-101£®
[8]´ÞÐÇ£¬ÈòÇ嶫£®»ùÓÚÖ¡¼ä²î·Ö·½·¨µÄµÀ·³µÁ¾¼ì²âϵͳ[J]£®Î¢¼ÆËã»úÐÅÏ¢£¬2007£¬23(4-1)£®
[9]Ñîºé·â£®ÊÓƵÐòÁÐÖÐÔ˶¯Ä¿±êµÄʵʱ·Ö¸îÓë¸ú×Ù£®[˶ʿѧλÂÛÎÄ]£®Î÷°²£ºÎ÷°²Àí¹¤´óѧ£®2003£®3£º19-21
[10]GANDHIT£¬DEVADIGAS£¬KASTURIR£¬eta1£®Detection of Obstacles on Runways using Ego-motion Compensation and Tracking of Significant Features[J]Int£®J£®of Image and Vi sion Computing£¬2000£¬18(10)£º302-303£®
[11]ÑîÎĽܣ¬ºúÃ÷Î⣬Ñî¾²ÓһÖÖ»ùÓÚ¹âÁ÷µÄÕÏ°Îï¹À¼ÆËã·¨[J]£®¼ÆËã»ú¹¤³ÌÓëÓ¦Óã¬2006£¬42(5)£º80-81£®
[12]ÖìÔÆ·¼£¬ÍõêÝÊõ£¬¶Å짣®¾²Ì¬»·¾³ÖлùÓÚ¹âÁ÷µÄÕÏ°Îï¼ì²â[J]£®Õã½´óѧѧ±¨£¬2008£¬42(6)£®
[13]´Þ¸ß½¨£¬»ÆÒø»¨£¬ÌïÔ£¬»ùÓÚÁ¢ÌåÊÓ¾õµÄÇ°·½³µÁ¾Ì½²â[J]£®2005£¬13(9)£º890-899£®
[14]Ze hang Sun¡£George Bebis£¬Ronald Miller£®On-Road Vehicle Detection Using Evolutionary Gabor Filter Optimization. IEEE Transactions on Intell igent Transportation Systems£¬2005£¬6(2)£º125-137£®
[15]ÕÔÒ»±ø£¬ÍõÈÙ±¾£¬ÀîÁջԵȣ®»ùÓÚ¼¤¹âÀ×´ïµÄÎÞÈ˼ÝÊ»³µÇ°·½ÕÏ°Îï¼ì²â[J]£®½»Í¨Óë¼ÆËã»ú£¬2007£¬25(135)£®
[16]WANG Rongben. GU Bai yuan£¬JIN Li sheng£®Study on curb detection method based on 3D range image by laser radar£®Proceedings of the IEEE Intelligent Vehicles Symposium£¬USA£®2005£®
[17]ºòµÂÔ壬Àî¿ËÇ¿¡£ÐÂÐͳµÔØ̽²âÀ×´ïϵͳ¼¼ÊõÑо¿£®Öйú»úе¹¤³Ì£¬2004£¬15(21)..
