本文選題：無(wú)人機(jī)景象 + CenSurE-star特征　； 參考：《儀器儀表學(xué)報(bào)》2017年02期

【摘要】：針對(duì)傳統(tǒng)局部不變特征的景象匹配算法冗余點(diǎn)多、實(shí)時(shí)性差、抗幾何變換不突出的情況,提出基于CenSurE-star的無(wú)人機(jī)(UAV)景象匹配算法。首先采用Cen Sur E特征星型濾波器(CenSurE-star)提取基準(zhǔn)圖和實(shí)時(shí)圖中的特征點(diǎn),并生成FREAK二進(jìn)制描述符;然后將漢明距離作為特征點(diǎn)的相似性判定度量,采用K近鄰距離比值的方法提取匹配點(diǎn)對(duì);最后利用基于RANSAC的定位模型得到空間幾何變換關(guān)系,實(shí)現(xiàn)圖像匹配并獲取定位點(diǎn)經(jīng)緯坐標(biāo)。算法性能評(píng)價(jià)實(shí)驗(yàn)表明,本文算法不僅相對(duì)于SIFT、SURF、ORB算法,對(duì)各種變換具有更好的魯棒性,而且相對(duì)于改進(jìn)的SIFT、SURF算法處理時(shí)間有更大程度的縮短,算法定位誤差在0.8個(gè)像素內(nèi),尺度誤差在0.02倍內(nèi),旋轉(zhuǎn)角度誤差在0.04°內(nèi)�；谒惴ㄟM(jìn)行外場(chǎng)飛行實(shí)驗(yàn),實(shí)驗(yàn)證明算法定位精度較高,可以適應(yīng)地貌信息較少的環(huán)境,并能滿足無(wú)人機(jī)視覺(jué)輔助導(dǎo)航的需求。
[Abstract]:In view of the traditional local invariant feature matching algorithm with more redundant points, poor real-time performance and anti geometric transformation, the image matching algorithm based on CenSurE-star (UAV) is proposed. Firstly, the Cen Sur E feature star filter (CenSurE-star) is used to extract the datum map and the feature points in the real-time map, and the FREAK binary description is generated. Then, the Hamming distance is used as the similarity determination measure of the feature point, and the matching point pair is extracted by the method of K nearest neighbor distance. Finally, the spatial geometric transformation relationship is obtained by using the RANSAC based location model, and the image matching is realized and the positioning point latitude and longitude coordinates are obtained. The algorithm energy evaluation experiment shows that the algorithm is not only relative to SIFT in this paper. The SURF, ORB algorithm has better robustness for various transformations, and compared with the improved SIFT, the processing time of the SURF algorithm is greatly shortened. The algorithm localization error is within 0.8 pixels, the scale error is within 0.02 times and the rotation angle error is within 0.04 degrees. The field flight experiment based on the algorithm shows that the algorithm has a higher positioning accuracy. It can adapt to the environment with less geomorphologic information and meet the needs of UAV vision aided navigation.
【作者單位】： 桂林電子科技大學(xué)電子工程與自動(dòng)化學(xué)院;桂林航天工業(yè)學(xué)院無(wú)人機(jī)遙測(cè)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金(61361006) 廣西自然科學(xué)基金(2015GXNSFBA139251) 廣西自動(dòng)檢測(cè)技術(shù)與儀器重點(diǎn)實(shí)驗(yàn)室基金(YQ14203) 廣西高校無(wú)人機(jī)遙測(cè)重點(diǎn)實(shí)驗(yàn)室主任基金(WRJ2015ZR02)項(xiàng)目資助
【分類號(hào)】：TP391.41;V279
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本文編號(hào)：2046968