本文選題：工業(yè)互聯(lián)網 + 無人機��； 參考：《北京交通大學》2017年碩士論文

【摘要】：通過井下工業(yè)互聯(lián)網,可以對井下無人機等智能設備進行遠程管理,對實現(xiàn)無人采礦具有十分重要的意義。為了對井下無人機進行管理,需要實現(xiàn)礦井環(huán)境下的無人機自主定位和導航。同時定位與建圖(SLAM)算法可以通過傳感器對周圍環(huán)境信息進行觀測,對無人機進行精確定位。因此,本文針對井下巷道中行駛的無人機單目視覺SLAM算法進行了研究,以實現(xiàn)礦井環(huán)境下的無人機自主定位和導航。本文的主要成果如下:(1)針對較寬闊無障礙物的巷道以及較窄巷道分別提出在巷道頂壁設置帶有位置信息的二維碼以及在巷道壁兩側設置反光標識牌作為無人機引導路標。針對這兩種不同的巷道環(huán)境,分別創(chuàng)建了基于幾何-拓撲的井下巷道圖。(2)針對寬闊巷道環(huán)境,提出了一種含有位置信息的二維碼,根據(jù)邊緣檢測、擬合直線等算法得到二維碼的碼值信息,即二維碼的位置信息,實驗和仿真表明該算法能快速清晰地對二維碼進行識別。針對較窄巷道環(huán)境中設置的人工路標即反光標識牌,事先將每一個反光標識牌和其周圍的自然特征進行圖像獲取并放入庫中,提出了基于RANSAC的SIFT算法對井下無人機單目視覺所獲取的每一幀圖像進行特征提取,并與事先建立好的特征圖像庫進行匹配。實驗和仿真表明基于RANSAC的SIFT算法有很高的正確匹配率,可以根據(jù)特征圖像庫和離線地圖得到路標位置信息。(3)針對較寬闊無障礙物的巷道和二維碼無人機引導路標場景,提出了一種基于二維碼的井下無人機單目視覺PSOFastSLAM算法。仿真結果表明所提出的井下無人機PSOFastSLAM算法,有效改善了 FastSLAM定位算法粒子退化的問題,提高了井下無人機定位精度。(4)針對較窄巷道和反光標識牌引導路標場景,提出了一種基于反光標識牌的井下無人機單目視覺EKF-SLAM算法,通過已知路標得到的觀測信息對無人機位姿進行估計。仿真結果表明單目視覺EKF-SLAM算法可以對井下無人機進行精確定位。最終結果顯示,針對不同的巷道環(huán)境,采用不同的SLAM算法可以對面向工業(yè)互聯(lián)網的井下無人機進行精確定位,為后續(xù)工業(yè)互聯(lián)網對采集環(huán)境數(shù)據(jù)的井下無人機進行有效管理打下基礎。
[Abstract]:Through the underground industrial Internet, intelligent equipment such as underground UAV can be managed remotely, which is of great significance to the realization of unmanned mining. In order to manage the underground UAV, it is necessary to realize the autonomous positioning and navigation of the UAV in the mine environment. The simultaneous location and Mapping (slam) algorithm can accurately locate the UAV by using sensors to observe the surrounding environment information. Therefore, in order to realize autonomous positioning and navigation of UAV in mine environment, the single vision slam algorithm of unmanned aerial vehicle (UAV) driving in underground roadway is studied in this paper. The main achievements of this paper are as follows: (1) for the wider roadway without obstacles and the narrower roadway, a two-dimension code with position information on the top wall of the roadway and a reflective sign on both sides of the roadway wall are put forward respectively as the UAV guide sign. Aiming at these two different laneway environments, the underground roadway map based on geometry and topology is created respectively. (2) aiming at the wide roadway environment, a kind of two-dimensional code with location information is proposed, which is based on edge detection. The code value information of the two dimensional code, i.e. the position information of the two dimensional code, is obtained by fitting the straight line. The experiment and simulation show that the algorithm can recognize the two dimensional code quickly and clearly. In view of the manual signpost set in the narrower tunnel environment, that is, the reflective sign, each reflective sign and its surrounding natural features are obtained and put into the library. This paper presents a sift algorithm based on RANSAC for feature extraction of each frame of image acquired by Monocular vision of downhole UAV, and matches it with the pre-established feature image database. Experiments and simulations show that the sift algorithm based on RANSAC has a high correct matching rate and can be used to obtain the location information of road signs according to the feature image database and off-line map. (3) aiming at the wide roadway without obstacles and the two-dimension code UAV guiding signpost scene, A PSOFastSLAM algorithm for downhole UAV monocular vision based on two dimensional code is proposed. The simulation results show that the proposed PSOFastSLAM algorithm can effectively improve the particle degradation of the FastSLAM algorithm and improve the positioning accuracy of the downhole UAV. (4) for the narrower roadway and the reflective sign to guide the road sign scene, the simulation results show that the proposed algorithm can effectively improve the particle degradation of the FastSLAM algorithm and improve the positioning accuracy of the downhole UAV. This paper presents an EKF-SLAM algorithm for downhole UAV monocular vision based on reflective sign, which can estimate the UAV position and attitude through the observation information obtained from the known road signs. Simulation results show that Monocular vision EKF-SLAM algorithm can accurately locate downhole UAV. The final results show that different slam algorithms can be used to locate the downhole UAV for industrial Internet in different laneway environments. It lays the foundation for the following industrial Internet to manage the underground UAV which collects environmental data effectively.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TD67;V279

【參考文獻】

相關期刊論文 前10條

1 柴曉;;井下無人采礦技術裝備導航與控制關鍵技術[J];內蒙古煤炭經濟;2016年15期

2 沈蘇彬;楊震;;工業(yè)互聯(lián)網概念和模型分析[J];南京郵電大學學報(自然科學版);2015年05期

3 王偉;陳華慶;韓衛(wèi);;無人機自主導航控制的FastSLAM算法研究[J];計算機仿真;2015年08期

4 薛永勝;王Y，

本文編號：2073370