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研究生:羅丞淵
研究生(外文):Cheng-YuanLuo
論文名稱:利用自駕車載光達於道路資訊特徵萃取控制分析
論文名稱(外文):Control Analysis of Road Information Feature Extraction Using Autonomous Vehicle LiDAR
指導教授:余騰鐸余騰鐸引用關係
指導教授(外文):Teng-To Yu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資源工程學系
學門:工程學門
學類:材料工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:93
中文關鍵詞:自駕車載光達高精地圖道路資訊特徵萃取
外文關鍵詞:autonomous vehicle LiDARHigh Definition mapnear real time reality mappingroad informationfeature extraction
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隨著科技的進步,自動駕駛正準備踏入人們的生活之中。在政府的政策引導下,台灣的自動駕駛車發展已開始進行。而自駕車能夠順利上路,必須仰賴高精地圖的建置。台灣政府為推動自動駕駛技術以及高精地圖的發展,內政部於2018成立高精地圖研究發展中心,並訂定相關規範以推動台灣自動駕駛以及高精地圖發展。
但高精地圖在建置及維護上目前有許多困難,為了符合自動駕駛的標準,高精地圖須達到高精確度以及即時更新,因此在建置初期的成本非常高昂,再加上圖資需要包含許多道路屬性,包含車道線、標誌、交通號誌等,而道路資訊的變化頻率高,要如何在高動態變化的道路情境下進行高頻率的更新及應用,是許多產業及學術研究的目標。
本研究利用自駕車上之光學雷達(LiDAR)於道路資訊特徵萃取控制分析為目標,將自駕車載光達於臺灣智駕測試實驗室中掃描之點雲利用常態分佈轉換法建立點雲地圖,並於同一特徵物不同次掃描之點雲利用迭代最鄰近點法進行疊合,而後針對二維路面標線以及三維路旁物體利用Canny邊緣萃取法以及Poisson曲面重建進行精度以及辨識力的計算。
結果顯示在二維路面標線中於平均車速15km/h下掃描距離位於2m之內、三維路旁物體中桿狀特徵於平均車速15km/h下掃描距離位於5m內時可以達到符合高精地圖標準的精度及不錯的辨識力。在疊圖層數較低時容易因疊圖偏移而造成辨識力下降,但若是有更多資料進行疊加,應能夠使得特徵萃取的能力更加提升。
Advances in science and technology that makes the autonomous driving becoming a reality. The development policy of the autonomous vehicles in Taiwan has begun and the High Definition maps (HD maps) are the key to the success of autonomous driving. In order to promote the development of autonomous driving technology and HD maps, the Ministry of the Interior of Taiwan has established the High Definition Maps Research Center in 2018. However, there are still many difficulties in the construction and maintenance of HD maps. To meet the standards of autonomous driving, HD maps must possess high accuracy quality and also could be real-time updates. Therefore, the initial cost of construction is very high so it the maintain. The HD maps contain many elements, including lane borders, traffic signs, objects, etc., which change frequently. How to update in time and also offer apply high resolution information under dramatic altering conditions is the goal of many industries and researches. In this study, we used the low-end LiDAR system on broad the autonomous vehicle to extract and analyze the road information features. The scanned point cloud from the Taiwan CAR Lab and then use the normal distribution transform to create a point cloud data layer. Then we used the iterative closet point to align point clouds from different scans of the same features. Finally, we used Canny edge extraction and Poisson surface reconstruction to extract features for 2D road markings and 3D roadside objects and calculated their accuracy and recognition ability. The results showed that the proposed method could achieve the accuracy that met the standards of HD maps and also gain a good recognition ability for the traffic signs and nearby objects, when the scanning distance is within 5m at an average speed of 15km/h. By overlap multi LiDAR data set from various pass of vehicles, the data density should be elaborated to fulfill the need of detail mapping. Social share point clouds could make the update of HD maps within minutes and offer near real time reality mapping capability.
摘要 I
Abstract II
誌謝 V
目錄 VI
表目錄 IX
圖目錄 XI
第一章 緒論 1
1.1 前言 1
1.2 研究目的與動機 2
1.3 研究流程 3
第二章 文獻回顧 4
2.1 高精地圖 4
2.1.1 高精地圖發展趨勢 5
2.1.2 高精地圖產製流程 6
2.1.3 高精地圖的更新 7
2.2 光達系統 10
2.2.1 光達基本原理 10
2.2.2 車載光達系統 12
2.2.3 自駕車光達 13
2.3 道路資訊處理 15
2.3.1 點雲匹配 15
2.3.2 特徵萃取 16
第三章 研究區域與方法 18
3.1 研究區域概述 18
3.2 研究資料與工具 20
3.3 點雲資料處理概述 22
3.3.1 常態分佈轉換 22
3.3.2 迭代最近鄰近點 27
3.4 特徵萃取法概述 30
3.4.1 Canny 邊緣萃取 30
3.4.2 Poisson曲面重建 35
3.5 偵測能力檢驗 38
3.5.1 BF score 38
第四章 結果與討論 41
4.1 二維路面標線 41
4.1.1 指向線 42
4.1.2 斑馬線 46
4.1.3 鐵路標字 50
4.2 三維路旁物體 54
4.2.1 變電箱 54
4.2.2 路燈 59
4.2.3 柵欄 65
4.3 特徵物件比較 70
4.3.1 二維路面標線比較 70
4.3.2 三維路旁物體比較 74
4.4 綜合討論 79
4.4.1 點雲資料分析 79
4.4.2 疊圖分析 80
4.4.3 特徵萃取法及檢驗分析 81
4.4.4 特徵物分析 82
4.4.5 感測器分析 83
第五章 結論與建議 86
5.1 結論 86
5.2 建議 87
第六章 參考文獻 89
1.台灣資通產業標準協會,高精地圖製圖作業指引,2018。
2.台灣資通產業標準協會,高精地圖圖資內容及格式標準v1.0,2020。
3.台灣資通產業標準協會,高精地圖檢核及驗證指引,2020。
4.石育賢,台灣自駕車產業規劃與發展策略,機械工業 433期,14-26,2019。
5.汪聖倫,基於光達之多感測器融合定位系統於高動態自動駕駛之研究,國立成功大學電機工程學系,2019。
6.張智安、連以諾、蔡富安、陳宗杰,,行動測量系統於道路資訊萃取應用,科儀新知,第三十五卷,第一期,55-65,2013。
7.莊子毅、趙鍵哲,光達點雲幾何特徵萃取及匹配,航測及遙測學刊 第二十卷 第二期,102-128,2016。
8.陳威廷,結合光達相機與雷達之追蹤演算法發展及其於自駕車之應用,國立成功大學電機工程學系,2019。
9.曾芷晴、徐珮晴、張清鴻、李育華、江凱偉、王靚琇,自駕車使用之高精度地圖規範擬定與資料蒐集處理,航測及遙測學刊,第二十四卷 第三期,173-182,2019。
10.詹智翔,使用車載光達進行路面平整度偵測可行性研究,國立成功大學資源工程學系,2013。
11.蔡慶璋,以空載光達資料自動化偵測蝕溝空間特徵與發育潛勢評估,國立成功大學資源工程學系,2019。
12.鄭錦桐、車文韜、岡田泰征、廖玲琬,高精地圖製作與交通運輸商業應用趨勢,土木水利 第四十六卷,第二期,51-58,2019。
13.謝汶霖,結合點雲之多感知融合定位系統於自駕車之研究,國立成功大學電機工程學系,2018。
14.羅英哲、曾義星,光達點雲資料面特徵重建,航測及遙測學刊 第十四卷,第三期,171-184,2009。
15.Bellekens, B., Spruyt, V., Berkvens, R., & Weyn, M. A survey of rigid 3d pointcloud registration algorithms. In AMBIENT 2014: the Fourth International Conference on Ambient Computing, Applications, Services and Technologies , 8-13, 2014.
16.Besl, P. J., & McKay, N. D. Method for registration of 3-D shapes. In Sensor fusion IV: control paradigms and data structures , 1611, 586-606, 1992.
17.Biber, P., & Straßer, W. The normal distributions transform: A new approach to laser scan matching. In Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), 3, 2743-2748, 2003.
18.Canny, J. F. A theory of edge detection. IEEE Trans Pattern Anal Mach Intell, 8, 147-163, 1986.
19.Choi, J., Ulbrich, S., Lichte, B., & Maurer, M. Multi-target tracking using a 3d-lidar sensor for autonomous vehicles. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 881-886, 2013.
20.Dannheim, C., Maeder, M., Dratva, A., Raffero, M., Neumeier, S., & Icking, C. A cost-effective solution for HD Maps creation. In AmE 2017-Automotive meets Electronics; 8th GMM-Symposium, 1-5, 2017.
21.Dassot, M., Constant, T., & Fournier, M. The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Annals of forest science, 68(5), 959-974, 2011.
22.Dewez, T., Girardeau-Montaut, D, Allanic, C., & Rohmer J. Facets : A CloudCompare Plugin to Extract Geological Planes from Unstructed 3D Point Clouds, XXIII ISPRS Congress, 799-804 , 2016.
23.Ghallabi, F., Nashashibi, F., El-Haj-Shhade, G., & Mittet, M. A. Lidar-based lane marking detection for vehicle positioning in an hd map, In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2209-2214, 2018.
24.Harrison, J. W., Iles, P. J., Ferrie, F. P., Hefford, S., Kusevic, K., Samson, C., & Mrstik, P. Tessellation of ground-based LIDAR data for ICP registration. In 2008 Canadian Conference on Computer and Robot Vision, 345-351, 2008.
25.Hiremagalur, J., Yen, K. S., Lasky, T. A., & Ravani, B. Testing and performance evaluation of fixed terrestrial three-dimensional laser scanning systems for highway applications. Transportation research record, 2098(1), 29-40, 2009.
26.Jaw, J. J., & Chuang, T.-Y. Feature-based registration of LiDAR point clouds. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 37, 303-308, 2008.
27.Jo, K., Kim, C., & Sunwoo, M. Simultaneous localization and map change update for the high definition map-based autonomous driving car, Sensors, 18(9), 3145, 2018.
28.Kazhdan, M., Bolitho, M., & Hoppe, H. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, 7, 2006.
29.Liebner, M., Jain, D., Schauseil, J., Pannen, D., & Hackelöer, A. Crowdsourced HD Map Patches Based on Road Model Inference and Graph-Based SLAM, In 2019 IEEE Intelligent Vehicles Symposium (IV), 1211-1218, 2019.
30.Massow, K., Kwella, B., Pfeifer, N., Häusler, F., Pontow, J., Radusch, I., ... & Haueis, M. Deriving HD maps for highly automated driving from vehicular probe data, In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1745-1752, 2016.
31.Poggenhans, F., Pauls, J. H., Janosovits, J., Orf, S., Naumann, M., Kuhnt, F., & Mayr, M. Lanelet2: A high-definition map framework for the future of automated driving. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 1672-1679, 2018.
32.Puente, I., González-Jorge, H., Martínez-Sánchez, J., & Arias, P. Review of mobile mapping and surveying technologies. Measurement, 46(7), 2127-2145, 2013.
33.Shan, J., & Toth, C. K. Topographic laser ranging and scanning: principles and processing. CRC press, 2018.
34.Stephenson, S., Meng, X., Moore, T., Baxendale, A., & Ford, T. Accuracy requirements and benchmarking position solutions for intelligent transportation location based services. In Proceedings of the 8th international symposium on location-based services, 2011.
35.Toth, C. K. R&D of mobile LiDAR mapping and future trends. In Proc. ASPRS 2009 Annual Conference, 9-13, 2009.
36.Wehr, A., & Lohr, U. Airborne laser scanning—an introduction and overview. ISPRS Journal of photogrammetry and remote sensing, 54(2-3), 68-82, 1999.
37.Yang, B., Fang, L., & Li, J. Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 80-93, 2003.
38.Yang, B., Fang, L., Li, Q., & Li, J. Automated extraction of road markings from mobile LiDAR point clouds. Photogrammetric Engineering & Remote Sensing, 78(4), 331-338, 2012.
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