室內(nèi)機器人的同步定位與建圖方法的研究
發(fā)布時間：2018-07-22 13:00
【摘要】：同步定位與建圖(SLAM)技術(shù)作為機器人科學(xué)中的一個重要領(lǐng)域,在制造業(yè),農(nóng)業(yè),醫(yī)療衛(wèi)生業(yè),服務(wù)業(yè),國防工業(yè)等眾多領(lǐng)域有著廣泛的應(yīng)用。其中室內(nèi)機器人領(lǐng)域更是其中的一個熱點。然而傳統(tǒng)的室內(nèi)機器人RGB-D SLAM由于在提取和匹配特征的時候計算開銷大,所以算法的實時性并不理想。同時由于深度相機的結(jié)構(gòu)特性,使得在邊緣處的深度信息獲取不完全。而大部分的特征都處于物體的邊緣處,這種情況使得系統(tǒng)在計算機器人的位姿時,很難找到足夠多的特征點,容易造成位姿的丟失。提出了一種結(jié)合顯著性特征篩選的改進ORB算法。針對傳統(tǒng)的RGB-D SLAM提取和匹配特征時計算量過大的問題,本文采用了ORB特征以減輕計算量,并通過盒狀濾波器構(gòu)建了尺度空間,使得ORB的尺度不變性得到了一定的提升。為了解決誤匹配較多影響效率的問題,本文提出了一種結(jié)合視覺顯著性特征的特征點篩選方法,本文采用基于顏色統(tǒng)計的高效空間顯著圖計算方法來計算每個關(guān)鍵點的顯著值,同時通過特征點自身以及周圍點的顯著值來對特征點的匹配進行對比,將差距過大的點篩選除去,提高了匹配的精度和速度。提出了一種通過遠近點分類和光束平差法計算RGB-D SLAM中的機器人位姿的方法。通過將特征點按照遠近進行分類,對于深度可信的點直接獲取深度;對于深度不可信的點采用多幀方式估算出深度信息,從而豐富地圖中的關(guān)鍵點。通過光束平差法對空間點進行重映射構(gòu),用誤差函數(shù)優(yōu)化并求解出機器人的旋轉(zhuǎn)和平移,同時結(jié)合改進ORB特征和顯著性匹配,構(gòu)建SLAM系統(tǒng),使得位姿計算時對于邊緣深度值的缺失有一定的魯棒性。本文在TUM的SLAM公開數(shù)據(jù)集上進行了驗證,試驗結(jié)果表明,本文的方法可以有效地在室內(nèi)環(huán)境下進行同步定位與建圖。對于存在動態(tài)模糊的場景,本文的方法也可以取得很好的效果。
[Abstract]:Synchronous Positioning and Mapping (slam) technology, as an important field in robot science, has been widely used in many fields, such as manufacturing, agriculture, medical and health industry, service industry, national defense industry and so on. The field of indoor robot is one of the hot spots. However, the traditional indoor robot RGB-D slam is not ideal for its real-time performance due to its high computational cost in feature extraction and matching. At the same time, because of the structural characteristics of the depth camera, the depth information at the edge is incomplete. However, most of the features are located at the edge of the object, which makes it difficult for the system to find enough feature points in the calculation of the robot's position and posture, which can easily lead to the loss of the position and pose. An improved Orb algorithm combining salience feature filtering is proposed. In order to solve the problem that the traditional RGB-D slam has too much computation to extract and match features, this paper adopts Orb feature to reduce the computation load, and constructs the scale space through the box filter, which improves the scale invariance of Orb to a certain extent. In order to solve the problem that mismatch affects efficiency more, this paper proposes a feature point selection method combining visual saliency features. In this paper, the significant value of each key point is calculated by using an efficient spatial saliency map calculation method based on color statistics. At the same time, the matching of feature points is compared by the salient values of the feature points themselves and the surrounding points, and the selection of points with too large a gap is removed, which improves the accuracy and speed of the matching. In this paper, a method of calculating robot pose in RGB-D slam by far and near point classification and beam adjustment method is proposed. By classifying the feature points according to the distance and near, the depth can be obtained directly for the points with confidence in depth, and the depth information can be estimated by multi-frame method for the points that are not trusted in depth, thus enriching the key points in the map. The spatial points are remapped by the beam adjustment method, and the rotation and translation of the robot are optimized and solved by the error function, and the slam system is constructed by combining the improved Orb feature and salience matching. So that the position and pose calculation is robust to the absence of edge depth value. This paper is validated on the slam open data set of TUM. The experimental results show that the proposed method can be used to locate and map synchronously in indoor environment. For the scene with dynamic ambiguity, the method in this paper can also achieve good results.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242
本文編號：2137542
[Abstract]:Synchronous Positioning and Mapping (slam) technology, as an important field in robot science, has been widely used in many fields, such as manufacturing, agriculture, medical and health industry, service industry, national defense industry and so on. The field of indoor robot is one of the hot spots. However, the traditional indoor robot RGB-D slam is not ideal for its real-time performance due to its high computational cost in feature extraction and matching. At the same time, because of the structural characteristics of the depth camera, the depth information at the edge is incomplete. However, most of the features are located at the edge of the object, which makes it difficult for the system to find enough feature points in the calculation of the robot's position and posture, which can easily lead to the loss of the position and pose. An improved Orb algorithm combining salience feature filtering is proposed. In order to solve the problem that the traditional RGB-D slam has too much computation to extract and match features, this paper adopts Orb feature to reduce the computation load, and constructs the scale space through the box filter, which improves the scale invariance of Orb to a certain extent. In order to solve the problem that mismatch affects efficiency more, this paper proposes a feature point selection method combining visual saliency features. In this paper, the significant value of each key point is calculated by using an efficient spatial saliency map calculation method based on color statistics. At the same time, the matching of feature points is compared by the salient values of the feature points themselves and the surrounding points, and the selection of points with too large a gap is removed, which improves the accuracy and speed of the matching. In this paper, a method of calculating robot pose in RGB-D slam by far and near point classification and beam adjustment method is proposed. By classifying the feature points according to the distance and near, the depth can be obtained directly for the points with confidence in depth, and the depth information can be estimated by multi-frame method for the points that are not trusted in depth, thus enriching the key points in the map. The spatial points are remapped by the beam adjustment method, and the rotation and translation of the robot are optimized and solved by the error function, and the slam system is constructed by combining the improved Orb feature and salience matching. So that the position and pose calculation is robust to the absence of edge depth value. This paper is validated on the slam open data set of TUM. The experimental results show that the proposed method can be used to locate and map synchronously in indoor environment. For the scene with dynamic ambiguity, the method in this paper can also achieve good results.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242
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