本文選題：電力巡檢機器人 + 角點特征�。� 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：點云地圖的創(chuàng)建算法是搭載2D Lidar的智能電力巡檢機器人領(lǐng)域中一項關(guān)鍵性技術(shù),點云地圖的精度高低會直接影響到巡檢機器人在工作過程中定位的精確度,進而影響到巡檢機器人運動狀態(tài)的更新以及路徑規(guī)劃的進行,是巡檢機器人實現(xiàn)自主移動的根基所在,其重要性不言而喻。ICP算法是在創(chuàng)建點云地圖過程中常用的一種算法,但是僅依靠ICP算法建立的點云地圖隨著地圖建立時間的變長和地圖覆蓋范圍的增大,其累積誤差將會變得非常嚴重。閉環(huán)檢測作為一種可以有效減小累積誤差的手段,得到了國內(nèi)外很多學(xué)者的廣泛研究。閉環(huán)檢測中的一個核心問題是地點識別,即能檢測到在之前已經(jīng)到過同一地點附近。解決地點識別問題的一種有效方法是提取單幀數(shù)據(jù)中的特征點,利用特征點來反映兩幀數(shù)據(jù)之間的相似性。因此,如何設(shè)計針對2D Lidar的特征提取算法,以及如何利用提取出的特征來檢索相似幀對于解決地點識別問題有著很明確的研究價值。因此本文第二章和第三章針對這兩個問題展開。針對第一個問題,考慮到在實際環(huán)境中廣泛存在的諸如建筑物墻角、桌角等穩(wěn)定的角點特征,本文提出了一種基于2D Lidar的角點特征提取算法。算法結(jié)合兩點間的歐式距離和相應(yīng)法向量間的余弦距離雙閾值來確定單幀點云中每點的鄰域范圍,具體而言,以較大的歐式距離閾值來確定粗略的鄰域范圍,再以較小的余弦距離來確定更加精準的鄰域范圍。同時為了更好地將角點從點云中提取出來,本文給出了一種新穎的評價函數(shù),可以有效地檢測出準確的角點。在網(wǎng)上公開的數(shù)據(jù)庫上進行的對比實驗顯示本文所提出的角點特征提取算法的準確性較其他算法要更好。針對第二個問題,本文提出了基于2D Lidar角點特征的閉環(huán)算法。首先利用第二章中提出的針對2D Lidar的角點特征提取算法來獲得單幀數(shù)據(jù)的簽名,緊接著設(shè)計了一種相似幀判定方法讓簽名具有旋轉(zhuǎn)不變性,同時給出了相似幀之間的相對位姿的計算方法,建立圖模型,最后結(jié)合現(xiàn)有的圖優(yōu)化框架來對圖模型進行后端優(yōu)化。在網(wǎng)上公開數(shù)據(jù)庫上的實驗表明經(jīng)過本文所提出的閉環(huán)算法優(yōu)化后的點云地圖相比未經(jīng)優(yōu)化的點云地圖效果明顯要更好。最后,針對與大立科技公司合作的電力巡檢機器人建圖及導(dǎo)航項目,本論文開發(fā)了一套結(jié)合建圖、路徑規(guī)劃、實時導(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,得到了很好的實用效果。目前該系統(tǒng)已經(jīng)通過客戶單位驗收并交付使用。
[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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242

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本文編號：1840530