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基于立體視覺的智能農(nóng)業(yè)車輛實(shí)時(shí)運(yùn)動(dòng)檢測
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國家高技術(shù)研究發(fā)展計(jì)劃(863計(jì)劃)資助項(xiàng)目(2006AA10Z259);中央高校基本科研業(yè)務(wù)費(fèi)資助項(xiàng)目(KYZ201006);南京農(nóng)業(yè)大學(xué)青年科技創(chuàng)新基金資助項(xiàng)目(KJ09030)


Real-time Motion Detection for Intelligent Agricultural Vehicle Based on Stereo Vision
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    摘要:

    為滿足智能農(nóng)業(yè)車輛的精確導(dǎo)航,,提出基于立體視覺的車輛實(shí)時(shí)運(yùn)動(dòng)檢測方案,。該方案通過多線程特征點(diǎn)檢測提高傳統(tǒng)SIFT特征檢測算法的效率,,通過歸一化綜合距離法剔除誤匹配的特征點(diǎn),,最后通過相鄰時(shí)刻同一特征點(diǎn)坐標(biāo)的變化反推車輛的運(yùn)動(dòng),。試驗(yàn)表明:多線程SIFT特征點(diǎn)檢測能夠縮短檢測時(shí)間,,提高計(jì)算效率,。歸一化綜合距離法能夠有效剔除傳統(tǒng)SIFT算法的誤匹配點(diǎn),。當(dāng)車速為0.8m/s,,圖像采集頻率為5Hz時(shí),車輛在x方向和z方向單次測量誤差小于0.0045m,,當(dāng)持續(xù)運(yùn)動(dòng)時(shí)間達(dá)到10s時(shí),,2個(gè)方向累積測量誤差均小于0.15m。

    Abstract:

    A real-time motion detection method based on stereo vision was designed in order to meet the need of precise navigation for intelligent agricultural vehicle. Multi-thread feature points detection was used to improve the efficiency of traditional SIFT algorithm. Normalized comprehensive distance algorithm (NCDA) was used to delete the error matched points. Finally, vehicle motion was calculated by the position variation of feature points in adjacent moment. Experiments showed that the average detection time spent was reduced and computing efficiency was raised up. Error matched points were successfully recognized and erased by NCDA. Measurement errors in one time were less than 0.0045m in x and z axis when the speed was 0.8m/s and image grabbing frequency was 5Hz. Measurement errors in x andzaxis were less than 0.15m when the vehicle kept moving for 10s.

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田光兆,安秋,姬長英,顧寶興,王海青,趙建東.基于立體視覺的智能農(nóng)業(yè)車輛實(shí)時(shí)運(yùn)動(dòng)檢測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2013,44(7):210-215. Tian Guangzhao, An Qiu, Ji Changying, Gu Baoxing, Wang Haiqing, Zhao Jiandong. Real-time Motion Detection for Intelligent Agricultural Vehicle Based on Stereo Vision[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(7):210-215.

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  • 在線發(fā)布日期: 2013-06-20
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