本文選題：電力巡檢機(jī)器人 + 角點(diǎn)特征　； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：點(diǎn)云地圖的創(chuàng)建算法是搭載2D Lidar的智能電力巡檢機(jī)器人領(lǐng)域中一項(xiàng)關(guān)鍵性技術(shù),點(diǎn)云地圖的精度高低會(huì)直接影響到巡檢機(jī)器人在工作過(guò)程中定位的精確度,進(jìn)而影響到巡檢機(jī)器人運(yùn)動(dòng)狀態(tài)的更新以及路徑規(guī)劃的進(jìn)行,是巡檢機(jī)器人實(shí)現(xiàn)自主移動(dòng)的根基所在,其重要性不言而喻。ICP算法是在創(chuàng)建點(diǎn)云地圖過(guò)程中常用的一種算法,但是僅依靠ICP算法建立的點(diǎn)云地圖隨著地圖建立時(shí)間的變長(zhǎng)和地圖覆蓋范圍的增大,其累積誤差將會(huì)變得非常嚴(yán)重。閉環(huán)檢測(cè)作為一種可以有效減小累積誤差的手段,得到了國(guó)內(nèi)外很多學(xué)者的廣泛研究。閉環(huán)檢測(cè)中的一個(gè)核心問(wèn)題是地點(diǎn)識(shí)別,即能檢測(cè)到在之前已經(jīng)到過(guò)同一地點(diǎn)附近。解決地點(diǎn)識(shí)別問(wèn)題的一種有效方法是提取單幀數(shù)據(jù)中的特征點(diǎn),利用特征點(diǎn)來(lái)反映兩幀數(shù)據(jù)之間的相似性。因此,如何設(shè)計(jì)針對(duì)2D Lidar的特征提取算法,以及如何利用提取出的特征來(lái)檢索相似幀對(duì)于解決地點(diǎn)識(shí)別問(wèn)題有著很明確的研究?jī)r(jià)值。因此本文第二章和第三章針對(duì)這兩個(gè)問(wèn)題展開(kāi)。針對(duì)第一個(gè)問(wèn)題,考慮到在實(shí)際環(huán)境中廣泛存在的諸如建筑物墻角、桌角等穩(wěn)定的角點(diǎn)特征,本文提出了一種基于2D Lidar的角點(diǎn)特征提取算法。算法結(jié)合兩點(diǎn)間的歐式距離和相應(yīng)法向量間的余弦距離雙閾值來(lái)確定單幀點(diǎn)云中每點(diǎn)的鄰域范圍,具體而言,以較大的歐式距離閾值來(lái)確定粗略的鄰域范圍,再以較小的余弦距離來(lái)確定更加精準(zhǔn)的鄰域范圍。同時(shí)為了更好地將角點(diǎn)從點(diǎn)云中提取出來(lái),本文給出了一種新穎的評(píng)價(jià)函數(shù),可以有效地檢測(cè)出準(zhǔn)確的角點(diǎn)。在網(wǎng)上公開(kāi)的數(shù)據(jù)庫(kù)上進(jìn)行的對(duì)比實(shí)驗(yàn)顯示本文所提出的角點(diǎn)特征提取算法的準(zhǔn)確性較其他算法要更好。針對(duì)第二個(gè)問(wèn)題,本文提出了基于2D Lidar角點(diǎn)特征的閉環(huán)算法。首先利用第二章中提出的針對(duì)2D Lidar的角點(diǎn)特征提取算法來(lái)獲得單幀數(shù)據(jù)的簽名,緊接著設(shè)計(jì)了一種相似幀判定方法讓簽名具有旋轉(zhuǎn)不變性,同時(shí)給出了相似幀之間的相對(duì)位姿的計(jì)算方法,建立圖模型,最后結(jié)合現(xiàn)有的圖優(yōu)化框架來(lái)對(duì)圖模型進(jìn)行后端優(yōu)化。在網(wǎng)上公開(kāi)數(shù)據(jù)庫(kù)上的實(shí)驗(yàn)表明經(jīng)過(guò)本文所提出的閉環(huán)算法優(yōu)化后的點(diǎn)云地圖相比未經(jīng)優(yōu)化的點(diǎn)云地圖效果明顯要更好。最后,針對(duì)與大立科技公司合作的電力巡檢機(jī)器人建圖及導(dǎo)航項(xiàng)目,本論文開(kāi)發(fā)了一套結(jié)合建圖、路徑規(guī)劃、實(shí)時(shí)導(dǎo)航功能的系統(tǒng),并將所研究的相關(guān)算法應(yīng)用到系統(tǒng)中,得到了很好的實(shí)用效果。目前該系統(tǒng)已經(jīng)通過(guò)客戶單位驗(yàn)收并交付使用。
[Abstract]:The algorithm of creating point cloud map is a key technology in the field of intelligent power inspection robot with 2D Lidar. The accuracy of point cloud map will directly affect the accuracy of location in the working process of the inspection robot. Furthermore, it affects the updating of the moving state and the path planning of the patrol robot. It is the foundation of the robot to realize the autonomous movement. The importance of ICP algorithm is self-evident. ICP algorithm is a common algorithm in the process of creating the point cloud map. However, the accumulated error of point cloud map based on ICP algorithm will become very serious with the increase of map establishment time and map coverage. Closed-loop detection, as an effective method to reduce the cumulative error, has been widely studied by many scholars at home and abroad. One of the key problems in closed-loop detection is location identification, which can detect that the location has been near the same location before. An effective method to solve the problem of location identification is to extract feature points from single frame data and use feature points to reflect the similarity between two frames of data. Therefore, how to design a feature extraction algorithm for 2D Lidar and how to use the extracted features to retrieve similar frames is of great value in solving the problem of location recognition. Therefore, the second and third chapters of this paper focus on these two problems. In view of the first problem, a corner feature extraction algorithm based on 2D Lidar is proposed in this paper, considering the stable corner features such as the corner of the building wall and the corner of the table, which are widely existed in the real environment. The algorithm combines the Euclidean distance between two points and the cosine distance between the corresponding normal vectors to determine the neighborhood range of each point in a single frame point cloud. In particular, a large Euclidean distance threshold is used to determine the rough neighborhood range. A smaller cosine distance is used to determine a more precise neighborhood range. At the same time, in order to extract the corner from the point cloud better, a novel evaluation function is given in this paper, which can detect the accurate corner effectively. A comparative experiment on a database published on the Internet shows that the proposed corner feature extraction algorithm is more accurate than other algorithms. To solve the second problem, a closed loop algorithm based on 2D Lidar corner feature is proposed. Firstly, the corner feature extraction algorithm for 2D Lidar is proposed in Chapter 2 to obtain the signature of single frame data, and then a similar frame decision method is designed to make the signature rotation-invariant. At the same time, the calculation method of the relative pose between similar frames is given, and the graph model is established. Finally, the back end of the graph model is optimized by combining the existing graph optimization framework. Experiments on the open database on the Internet show that the point cloud map optimized by the closed-loop algorithm proposed in this paper is more effective than the unoptimized point cloud map. Finally, in view of the power inspection robot mapping and navigation project of Dali Science and Technology Company, this paper develops a system which combines the functions of building map, path planning and real-time navigation, and applies the relevant algorithms to the system. Good practical results have been obtained. At present, the system has been accepted by the customer and delivered to use.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

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本文編號(hào)：1840530