臺灣博碩士論文加值系統

本研究開發一套基於電腦視覺修正全球地位系統的方法，精準降落於地面目標物ArUco圖案。四旋翼機自動降落的核心演算法分為電腦視覺及航行算則：電腦視覺定義目標物座標系將影像的對應點透過內方位參數投影到相機座標系，再以EPnP方法將世界座標系下參考點以及相機坐標系的投影點做3D與2D的轉換後，得到相機三維位置與姿態，便可求出相對目標物的相對距離；將上述得到之相對距離以航行算則計算成新經緯度誤差，便可與原始經緯度相增減，即可使無人機前往此點且自動降落。以四旋翼機掛載處理器及相機在單純和複雜環境、航點式自動降落及全程自動降落進行實測：單純環境中分別分析兩種ArUco圖案邊長尺寸的高度、降落誤差、經緯度變化的實驗數據並討論成果，不同的邊長尺寸影響著識別距離、影響在低高度時持續識別圖案的時間長短而導致降落誤差；複雜環境中為兩種相同邊長尺寸、不同ID的ArUco圖案與複雜圖案，實測通過多種圖案的安全檢測之視覺降落，分析飛行路徑的經緯度變化以及降落誤差。實測航點式自動降落，整合航點規劃與自動降落並實際飛行，並討論飛行中經緯度不同曲線之變化。全程自動降落則在不使用LAND模式下，加入小的邊寸尺寸ArUco圖案使無人機持續修正GPS至成功降落，並探討降落誤差的優化。

This thesis is to develop a method based on computer vision modifying global positioning system and the drone lands accurately on the ArUco pattern. The algorithm of the autonomous landing used for quadcopter is divided into two parts which contains computer vision and aviation formulary: computer vision deal with project correspondences from image coordinate to camera coordinate by using intrinsic matrix. Then using EPnP method to convert two-dimension projection points of the image coordinate system into three-dimension reference points of the world coordinate system. After obtaining the three-dimensional position and pose estimation of the camera, the relative distance between target and camera can be also obtained. In aviation formulary, the latitude and longitude error can be calculated by the relative distance. New latitude and longitude can be obtained by adding and subtracting the original and error. Finally, the drone can follow the new latitude and longitude and land automatically.
The quadcopter mounted processor and camera experimented in simple and complex environment, waypoint automatic landing and full automatic landing: In the simple environment, the experimental data of the height, landing error, latitude and longitude of the two size ArUco patterns are analyzed separately, and the results are discussed. The factor of different sizes affects the recognition distance and affects the landing error depending on the duration of action time when the pattern is continuously recognized at low height. In the complex environment, there are two same size, different ID ArUco patterns and complex patterns. The visual landslide of the safety detection of various patterns is measured and the changes of the latitude, longitude and landing error are analyzed. In waypoint automatic landing, integrating waypoint planning and automatic landing, experimenting in actual flight and discussing the changes in different latitude and longitude curves during flight. In the automatic landing, adding a small size ArUco pattern allows the drone to continuously correct the GPS before successfully landing without using LAND mode and explore the optimization of the landing error.

中文摘要 i
英文摘要 ii
誌謝 viii
目錄 ix
表目錄 xv
圖目錄 xvi
符號表 xxi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 5
1.3文獻回顧 5
1.4 研究方法與目的 11
1.5 論文架構 12
第二章 演算法之介紹 14
2.1 顏色模型 14
2.1.1 RGB色彩空間 14
2.1.2 灰階 15
2.2 相機模型 16
2.2.1相機座標系與影像座標系的關係－內方位矩陣 16
2.2.2世界座標系與相機座標系的關係－外方位矩陣 20
2.2.3三者座標系的關係 22
2.3相機校正 23
2.3.1透鏡畸變 23
2.3.2如何求得校正參數? 26
2.4降落圖像的識別 28
2.4.1影像分割及過濾 29
2.4.2 Otsu二值化與安全檢測 29
2.5 EPnP三維姿態估計 33
2.6航行算則 39
第三章 實驗設備介紹 41
3.1視覺系統設備 42
3.1.1樹梅派(Raspberry Pi) 42
3.1.2相機 (8MP Raspberry Pi Camera Module(v2)) 44
3.2視覺函示庫 44
3.2.1 OpenCV 44
3.2.2 ArUco 46
3.2.3 DroneKit 46
3.3飛行載具設備 47
3.3.1旋翼機 47
3.3.4飛行控制板 48
3.3.5 GPS Here+ 49
3.3.6地面資料站 49
3.4載具通訊方式 51
第四章 實驗方法與流程 52
4.1相機校正 53
4.1.1實驗目的 53
4.1.2實驗方法 53
4.1.3實驗設備 53
4.2安全檢測 54
4.2.1實驗目的 54
4.2.2實驗方法 54
4.3 EPnP三維姿態估計 54
4.3.1實驗目的 54
4.3.2實驗方法 54
4.3.3實驗設備 55
4.4 航行算則 55
4.4.1實驗目的 55
4.4.2實驗方法 55
4.4.3實驗設備 56
4.5相機識別圖案之距離限制 56
4.5.1實驗方法 56
4.5.2實驗流程圖 57
4.5.3實驗設備 57
4.6程式碼運算速度 58
4.6.1實驗目的 58
4.6.2實驗方法 58
4.6.3實驗設備 58
4.7室外飛行測試-單純環境 58
4.7.1實驗方法 58
4.7.2實驗流程圖 60
4.7.3實驗設備 60
4.8室外飛行測試-複雜環境 60
4.8.1實驗方法 60
4.8.2實驗流程圖 62
4.8.3實驗設備 62
4.9航點式自動降落 62
4.9.1實驗方法 62
4.9.2實驗流程圖 64
4.9.3實驗設備 64
4.10不同尺寸不同ID ArUco圖案自動降落 64
4.10.1實驗方法 64
4.10.2實驗流程圖 66
4.10.3實驗設備 66
第五章 實驗結果與分析 67
5.1相機校正 67
5.2安全檢測 68
5.3 EPnP三維姿態估計 69
5.4 航行算則 71
5.5相機識別圖案之距離限制 71
5.6程式碼運算速度 75
5.7室外飛行測試-單純環境 76
5.7.1邊長尺寸10公分 76
5.7.2邊長尺寸15公分 78
5.7.3與RTL(Return To Launch)模式比較 81
5.8 結果討論 86
5.9室外飛行測試-複雜環境 86
5.9.1邊長尺寸10公分 86
5.9.2邊長尺寸15公分 88
5.10 航點式自動降落 89
5.11 不同邊長尺寸不同ID ArUco 圖案自動降落 92
5.12 結果討論 94
第六章 結論與未來工作 95
6.1結論 95
6.2未來工作 96
參考文獻 97

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