本文選題：RGB-D建圖 + RGB-D��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：隨著科學(xué)技術(shù)、計(jì)算機(jī)網(wǎng)絡(luò)和硬件的進(jìn)步,同時(shí)定位與建圖(Simultaneous Localization and Mapping,SLAM)無(wú)疑已經(jīng)成為了移動(dòng)機(jī)器人智能化領(lǐng)域的研究熱點(diǎn)之一,它對(duì)機(jī)器人的自主移動(dòng)來(lái)說起到了至關(guān)重要的作用。SLAM可以使用很多方法實(shí)現(xiàn),總體上可以分為濾波器方法和圖優(yōu)化方法。對(duì)于利用圖像信息作為數(shù)據(jù)來(lái)源的SLAM問題被又稱為視覺SLAM(Visual SLAM,VSLAM)。在動(dòng)態(tài)、復(fù)雜度高和大尺度的環(huán)境下,利用視覺信息作為唯一的外部感知來(lái)源來(lái)解決SLAM問題是目前一個(gè)活躍的研究領(lǐng)域。針對(duì)該問題,本文的主要研究?jī)?nèi)容如下:首先,通過比較不同視覺傳感器的優(yōu)缺點(diǎn)并參考“圖優(yōu)化”方式構(gòu)建了基于深度相機(jī)的RGB-D建圖算法,并針對(duì)傳統(tǒng)視覺特征魯棒性、實(shí)時(shí)性和匹配精度較差的問題,提出了一種基于ORB視覺特征的RGB-D建圖算法。然后將不同視覺特征(ORB、SIFT、SURF、FAST、GridFAST等)應(yīng)用到RGB-D建圖算法中并比較了它們對(duì)整個(gè)建圖算法實(shí)時(shí)性、精度以及重定位能力的影響。實(shí)驗(yàn)證明,ORB特征在魯棒性、實(shí)時(shí)性和匹配精度方面的性能都遠(yuǎn)優(yōu)于其他視覺特征,基于ORB視覺特征的RGB-D建圖算法在實(shí)時(shí)性、建圖準(zhǔn)確性和重定位能力方面效果更好。其次,針對(duì)傳統(tǒng)關(guān)鍵幀選擇算法單一、整個(gè)SLAM過程中關(guān)鍵幀數(shù)量激增的問題,提出了一種改進(jìn)的關(guān)鍵幀選擇算法,并以此為基礎(chǔ)構(gòu)建了基于ORB特征的RGB-D SLAM算法。改進(jìn)的關(guān)鍵幀選擇算法不僅整合了幀間相對(duì)運(yùn)動(dòng)距離、特征點(diǎn)跟蹤以及最小視覺變化來(lái)選擇關(guān)鍵幀,同時(shí)檢測(cè)冗余關(guān)鍵幀并將其刪除。通過在RGB-D數(shù)據(jù)集上的實(shí)驗(yàn)表明,改進(jìn)的關(guān)鍵幀選擇算法能夠更精準(zhǔn)、及時(shí)地選擇關(guān)鍵幀,并在減少RGB-D SLAM算法中冗余關(guān)鍵幀的同時(shí)提高RGB-D SLAM算法的實(shí)時(shí)性和建圖、定位精度。最后,針對(duì)RGB-D相機(jī)使用靈活性較低、特征點(diǎn)法魯棒性較差的問題,利用直接法實(shí)現(xiàn)單目相機(jī)下的同時(shí)定位與建圖。該算法使用一般的單目相機(jī)為傳感器獲取環(huán)境信息,克服了深度相機(jī)只能在室內(nèi)環(huán)境下使用的局限性,也能在室外環(huán)境下使用。相較于特征點(diǎn)法,直接法直接對(duì)圖像的像素灰度進(jìn)行操作,能夠更加充分地使用環(huán)境中豐富的信息,在顯著特征不明顯時(shí)也能很好地估計(jì)深度。實(shí)驗(yàn)結(jié)果表明,在特征不明顯的情況下直接法仍適用。同時(shí),采用直接法的MonoSLAM算法,在室內(nèi)外環(huán)境下都能夠快速精準(zhǔn)的定位與建圖。
[Abstract]:With the progress of science and technology, computer network and hardware, simultaneous location and mapping has undoubtedly become one of the hot research topics in the field of intelligent mobile robot. It plays an important role in autonomous movement of robot. SLAM can be realized by many methods, which can be divided into filter method and graph optimization method. For the SLAM problem which uses image information as the data source, it is also called visual SLAM(Visual slam / VSLAMN. In dynamic, high complexity and large scale environments, the use of visual information as the sole source of external perception to solve the SLAM problem is an active research field. The main research contents of this paper are as follows: firstly, by comparing the advantages and disadvantages of different vision sensors and referring to the "graph optimization" method, we construct a RGB-D mapping algorithm based on depth camera, and aim at the robustness of traditional visual features. In this paper, a RGB-D mapping algorithm based on ORB visual features is proposed for the problems of poor real time performance and poor matching accuracy. Then, different visual features are applied to the RGB-D algorithm and their effects on the real-time, accuracy and relocation ability of the whole algorithm are compared. Experiments show that Orb features are far superior to other visual features in robustness, real-time performance and matching accuracy. The RGB-D mapping algorithm based on ORB visual features is more effective in real-time, mapping accuracy and repositioning ability. Secondly, aiming at the problem that the traditional key frame selection algorithm is single and the number of key frames increases rapidly in the whole SLAM process, an improved key-frame selection algorithm is proposed, and a RGB-D SLAM algorithm based on ORB features is constructed. The improved key-frame selection algorithm not only integrates the relative motion distance between frames, feature point tracking and minimum visual changes to select key frames, but also detects redundant key frames and removes them. The experiments on RGB-D dataset show that the improved key-frame selection algorithm can select key-frame more accurately and timely, and improve the real-time performance and mapping accuracy of RGB-D SLAM algorithm while reducing redundant key-frame in RGB-D SLAM algorithm. Finally, aiming at the problem that the RGB-D camera is less flexible in use and the robustness of the feature point method is poor, the direct method is used to realize simultaneous location and map building under the single camera. The algorithm uses the general monocular camera to obtain the environmental information for the sensor, which overcomes the limitation that the depth camera can only be used in the indoor environment, and can also be used in the outdoor environment. Compared with the feature point method, the direct method can directly operate the pixel grayscale of the image, which can make full use of the rich information in the environment, and can estimate the depth well when the salient features are not obvious. The experimental results show that the direct method is still applicable when the characteristics are not obvious. At the same time, the MonoSLAM algorithm of direct method can locate and build the map quickly and accurately in indoor and outdoor environment.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP242

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 梁明杰;閔華清;羅榮華;;基于圖優(yōu)化的同時(shí)定位與地圖創(chuàng)建綜述[J];機(jī)器人;2013年04期

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本文編號(hào)：1986659