現今製作高精度地圖主要乃利用配備光達之移動製圖系統，透過所收集而成的三維點雲資訊做為繪製的基礎。由於點雲資訊僅包含環境的三維座標，因此現今製作地圖的流程中尚需大量的人工，在點雲的基礎上繪製並給予物件的屬性。因此如何提升製圖效益、降低勞力需求並能導入到現有的產製地圖流程的技術研究為地圖供應商高度期盼的，因此，本研究整合了一個高效益移動製圖系統，其中硬體部分搭載非測量等級光達、消費等級相機、以及入門等級的定位暨慣性導航系統，結合提出的點雲後製流程，其中包含匯入控制點(GCPs)以及採用同時定位與地圖構建(SLAM)等有助於軌跡修正之技術重建出高精度的點雲成果。最後在後製流程中，本論文導入一個深度學習網路進行標牌的偵測，透過相機與光達間旋轉矩陣以及轉移矩陣的轉換，影像中的標牌中心點之大地座標即可被自動地萃取。實驗的結果顯示，所產製的點雲三維精度絕對均方根誤差(RMSE)可控制在10公分內，自動標牌萃取之絕對位置精度也可達到數十公分，因此本研究成果可顯著地利用可接受的硬體成本建置出高精度的點雲資訊，更進一步驗證自動萃取屬性導入製作高精度流程的可行性。

Lidar sensors are commonly equipped for HD map establishment on a mobile mapping system (MMS). However, the point clouds themselves do not contain object attributes. Therefore, human operators must manually obtain objects' positions to assign attributes for further high-definition map (HD Map) conversion, inevitably resulting in time-consuming processes and high labor costs. This dissertation presents a cost-effective MMS. The system comprises a non-survey grade Lidar, a commercial grade camera, and entry-level Global Navigation Satellite System/Inertial Navigation System (GNSS/INS). By incorporating ground control points (GCPs) with a Normal Distribution Transform Simultaneously Localization and Mapping (NDT SLAM) refinement and fluctuation refinement, both absolute position accuracy and relative position accuracy of the reconstructed point cloud can be secured. Meanwhile, a deep neural network for image detection is employed to obtain the bounding box of traffic signs. By applying the translation and rotation transformation between Lidar points and camera pixels, the intersection of the detected object in the image and Lidar scan points can be found. Experimental results show that point clouds can be reconstructed with an average 3D RMSE of less than 10cm. The center geodetic coordinates of traffic signs can be further extracted in sub-meter accuracy to reduce labor work in HD map establishment.

The Autoware Foundation [Online]. Available: https://www.autoware.org/ (accessed on 22 Sep 2021).
Drive PX2-autonomous driving platform. Available online: https://www.nvidia.com/ (accessed on 19 May 2020).
Riegl mobile mapping system. Available online: http://www.riegl.com/nc/products/mobile-scanning/ (accessed on 19 May 2020).
Leica mobile sensor platform. Available online: https://leica-geosystems.com/products/mobile-sensor-platforms/capture-platforms (accessed on 19 May 2020).
A Teledyne Technologies Mobile survey system. Available online: https://www.teledyneoptech.com/en/products/mobile-survey/ (accessed on 19 May 2020).
Trimble mobile mapping system. Available online: https://geospatial.trimble.com/products-and-solutions/mobile-mapping (accessed on 19 May 2020).
The Lanelet2 map format open library [Online]. Available: https://github.com/fzi-forschungszentrum-informatik/Lanelet2 (accessed on 22 Sep 2021).
The OPENDRIVE map format [Online]. Available: https://www.asam.net/standards/detail/opendrive/ (accessed on 22 Sep 2021).
Simulator CarSim [Online]. Available: https://www.carsim.com/products/carsim/ (accessed on 22 Sep 2021).
Simulator VTD [Online]. Available: https://vires.mscsoftware.com/ (accessed on 22 Sep 2021).
GIS tool ArcGIS [Online]. Available: https://www.arcgis.com/index.html (accessed on 22 Sep 2021).
GIS tool MicroStation [Online]. Available: https://www.bentley.com/en/products/brands/microstation (accessed on 22 Sep 2021).
HD Maps data contents and formats standard [Online]. Available: https://www.taics.org.tw/eng/Publishing.aspx?PubCat_id=2
M. Magnusson, A. J. Lilienthal, and T. Duckett, “Scan registration for autonomous mining vehicles using 3D-NDT,” Journal of Field Robotics, vol. 24, no. 10, pp. 803 – 827, Oct 2007.
Y. Yue, M. Gouda and K. El-Basyouny, "Automatic Detection and Mapping of Highway Guardrails from Mobile Lidar Point Clouds," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2520-2523, doi: 10.1109/IGARSS47720.2021.9553055.
J. Jeong, and A. Kim, “LiDAR Intensity Calibration for Road Marking Extraction,” International Confence on Ubiquitous Robots (UR), Honolulu, HI, USA, June 2018, pp. 455-460.
S. Niijima, J. Nitta, Y. Sasaki, and H. Mizoguchi, “Generating 3D fundamental map by large-scale SLAM and graph-based optimization focused on road center line,” IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Lisbon, Portugal, Sep. 2017, pp. 1188-1193.
B. Nagy, and C. Benedek, “CNN-Based Semantic Labeling Approach for Mobile Laser Scanning Data,” IEEE Sensors Journal, Vol. 19, no. 21, pp. 10034–10045, 2019.
X. Zhao, P. Sun, Z. Xu, H. Min, and H. Yu, “Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications,” IEEE Sensors Journal, Vol. 20, no. 9, pp. 4901-4913, 2020.
G. Wan, X. Yang, R. Cai, H. Li, H. Wang, and S. Song, “Robust and Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes,” IEEE International conference on Robotics and Automation (ICRA), Brisbane, Australia, May 2018, pp. 4670–4677.
T. Shamseldin, A. Manerikar, M. Elbahnasawy, and A. Habib, “SLAM-based Pseudo-GNSS/INS Localization System for Indoor LiDAR Mobile Mapping Systems,” IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA, Apr. 2018, pp. 197–208.
W. Neubauer, M. Doneus, and N. Studnicka, “Combined High Resolution Laser Scanning and Photogrammetrical Documentation of the Pyramids at Giza,” International Symposium CIPA, Torino, Italy 2005.
W. Moussa, M. Abdel-Wahab, and D. Fritsch, “Automatic Fusion of Digital Images and Laser Scanner Data for Heritage Preservation,” Progress in Cultural Heritage Preservation, Vol. 7616, Verlag, Berlin, Oct. 2012, pp. 76–85.
Y. An, B. Li, H. Hu, and X. Zhou, “Building an Omnidirectional 3D Color Laser Ranging System through a Novel Calibration Method,” IEEE Transactions on Industrial Electronics, Vol. 66, no. 11, pp. 8821 – 8831, 2019.
X. Zhang, X. Yu, W. Wan, J. Ma, Q. Lai, and L. Lu, “The Simplification of 3d color point cloud based on voxel,” IET International Conference on Smart and Sustainable City, Shanghai, China, Aug. 2013, pp. 442 – 445.
Y. H. Jo, and S. Hong, “Three-Dimensional Digital Documentation of Cultural Heritage Site Based on the Convergence of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry,” International Journal of Geo-Information, Vol. 8, no. 2, pp. 142 – 155, 2019.
M. Hulková, K. Pavelka, and E. Matoušková, “Automatic Classification of Point Clouds for Highway Documentation,” Acta Polytechnica, Vol. 58 no. 3, Jul. 2018, pp. 165–pp.170
N. Yastikli,“Documentation of cultural heritage using digital photogrammetry and laser scanning,”Journal of Cultural Heritage, Vol. 8 no. 4,Sep. 2007, pp. 423–pp.427
B. Alsadik, and Luma Khalid Jasim, “Active use of panoramic mobile mapping systems for as built surveying and heritage documentation,” International Journal of Architectural Heritage, Vol. 13 no. 2, Feb. 2018, pp. 244–pp.256
T. Amano, I. Miyagawa, and K. Murakami, “Full Color 3D Point Clouds from Bird's-eye View Using Multi-view Laser Scanner and Quadcopter,” International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, Thailand, Jan. 2018.
F. Zeng, and R. Zhong, “The algorithm to generate color point-cloud with the registration between panoramic image and laser point-cloud,” IOP Conference Series: Earth and Environmental Science 35th International Symposium on Remote Sensing of Environment (ISRSE35), Vol. 17, no. 1, Mar. 2013, pp. 22–26.
L. Yao, H. Wu, Y. Li, B. Meng, J. Qian, C. Liu, and H. Fan, “Regstration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations,” MDPI Sensors, Vol. 17, no. 4, pp. 837, 2017.
P. Vechersky, M. Cox, P. Borges, and T. Lowe, “Colourising Point Clouds using Independent Cameras,”IEEE Robotics and Automation Letters, Vol. 3 , no. 4, pp. 3575 – 3582, 2018.
N. Suttisangiam, and S. Bamrungpruk, “Software-based Timing Synchronization for Point Cloud Reconstruction,” International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Bangkok, Thailand, Oct. 2018, pp. 37 – 41.
S. Madeira, José A. Gonçalves, and L. Bastos, “Sensor Integration in a Low Cost Land Mobile Mapping System,” MDPI Sensors, Vol. 12, no. 3, Dec. 2012, pp. 2935–2953.
N. Haala, M. Petera, J. Kremerb, and G. Hunterc, “Mobile Lidar mapping for 3D point cloud collection in urban areas - A Performance Test,” International Society for Photogrammetry and Remote Sensing (ISPRS), 2008, pp. 1119–1124.
J. Kim, J. Jeong, Y. Shin, Y. Cho, H. Roh, and A. Kim, “LiDAR Configuration Comparison for Urban Mapping System,” 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Jeju, South Korea, 2017, pp. 854 – 857.
R. Ravi, Y.J. Ln, M lbahnasawy, T. Shamseldin, and A. Habib, “Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11, no. 5, pp. 1694 – 1714, 2018.
R. Nakashima; A. Seki,“Uncertainty-Based Adaptive Sensor Fusion for Visual-Inertial Odometry under Various Motion Characteristics,”, 2020 IEEE International Conference on Robotics and Automation (ICRA), Aug. 2020.
C. Won, H. Seok, Z. Cui, M. Pollefeys,and J. Lim,“Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems,”2020 IEEE International Conference on Robotics and Automation (ICRA), Aug. 2020.
Q. Zhu, Z. Wang, H. Hu, L. Xie, X. Ge, and Y. Zhang,“Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 166, Aug. 2020, pp. 26–pp.40.
M. Bosse, and R. Zlot, “Continuous 3D scan-matching with a spinning 2D laser,” IEEE International Conference on Robotics and Automation, Kobe, Japan, May 2009, pp. 4312 – 4319.
J. Zhang, and S. Singh, “LOAM: Lidar Odometry and Mapping in Real-time” Proceedings of Robotics: Science and Systems Conference, Berkeley, CA, Jul. 2014.
X. Liu, L. Zhang, S. Qin,D. Tian, S. Ouyang, and C. Chen, “Optimized LOAM Using Ground Plane Constraints and SegMatch-Based Loop Detection,” MDPI Sensors, Vol. 19, no. 24, 2019, pp:5419.
J-L. Jiao, “Machine Learning Assisted High-Definition Map Creation,” Computer Software and Applications Conference (COMPSAC), Tokyo, Japan, 23-27 July 2018.
F. Poggenhans, J-H Pauls, J. Janosovits, S. Orf, M. Naumann, F. Kuhnt, and M. Mayr, “Lanelet2: A hih-defintion map framework for the future of automated driving,” International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4-7 Nov. 2018.
D. Becker, F. Ruß, C. Geller and L. Eckstein, “Generation of Complex Road Networks Using a Simplified Logical Description for the Validation of Automated Vehicles,” International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 20-23 Sept. 2020.
M. Althoff, S. Urban and M. Koschi, “Automatic Conversion of Road Networks from OpenDRIVE to Lanelets,” International Conference on Service Operations and Logistics, and Informatics (SOLI), Singapore, 31 July-2 Aug. 2018.
E. Javanmardi, M. Javanmardi, Y. Gu and S. Kamijo, “Factors to Evaluate Capability of Map for Vehicle Localization,” in IEEE Access, vol. 6, pp. 49850–49867, 2018.
V. Ilci, C. Toth, “High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation,” Sensors, vol. 20, no. 3, pp. 899, Feb. 2020.
I. Hamieh, R. Myers and T. Rahman, “Construction of Autonomous Driving Maps employing LiDAR Odometry,” Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada, 5-8 May 2019.
C-W. Peng, C-C. Hsu and W-Y. Wang, “Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction,” Sensors, vol. 20, no. 22, pp. 6536, Nov. 2020.
K. Koide, J. Mura, M. Yokozuka, S. Oishi and A. Banno, “Interactive 3D Graph SLAM for Map Correction,” IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 40 – 47, Jan. 2021.
C. Kim, S. Cho, M. Sunwoo, P. Resende, B. Bradaï and K. Jo, “Updating Point Cloud Layer of High Definition (HD) Map Based on Crowd-Sourcing of Multiple Vehicles Installed LiDAR,” in IEEE Access, vol. 9, pp. 8028–8046, 2021.
A. Joshi and M. R. James, “Generation of Accurate Lane-Level Maps from Coarse Prior Maps and Lidar,” IEEE Intelligent Transportation Systems Magazine, vol. 7, no. 1, pp. 19–29, 2015.
P. Kumar, Conor P. McElhinney, P. Lewis and T. McCarthy, “Automated road markings extraction from mobile laser scanning data,” International Journal of Applied Earth Observation and Geoinformation, vol.32, pp. 125–137. 2014.
Y. Zhou, R. Huang, T. Jiang, Z. Dong and B. Yang, “Highway alignments extraction and 3D modeling from airborne laser scanning point clouds,” International Journal of Applied Earth Observation and Geoinformation, vol.102, 2021.
C. R. Qi, L. Yi, H. Su and L. J. Guibas, "PointNet++: Deep hierarchical feature learning on point sets in a metric space," Conference on Neural Information Processing Systems (NIPS), Ca., USA, Dec. 4-9, 2017, pp. 5100-5109.
T. Bi, Q. Liu, T. Ozcelebi, D. Jarnikov and D. Sekulovski, "PCANN: Distributed ANN Architecture for Image Recognition in Resource-Constrained IoT Devices," 2019 15th International Conference on Intelligent Environments (IE), 2019, pp. 1-8, doi: 10.1109/IE.2019.000-3.
D. Tabernik and D. Skoˇcaj, “Deep Learning for Large-Scale Traffic-Sign Detection and Recognition,” IEEE Transactions on Intelligent Transportation Systems. vol. 21, no. 4, pp. 1427 – 1440, May 2020.
C. Gerhardt and W. Broll, “Neural network-based traffic sign recognition in 360° images for semi-automatic road maintenance inventory,” International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 20-23 Sept. 2020.
C. Yan, C. Zheng, C. Gao, W. Yu, Y-Z. Cai and C-J. Ma, “Lane Information Perception Network for HD Maps,” International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 20-23 Sept. 2020.
M. Heo, J. Kim, S. Kim, “HD Map Change Detection with Cross-Domain Deep Metric Learning,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 24 Oct. - 24 Jan. 2021.
K. Jo, C. Kim and M. Sunwoo, “Simultaneous Localization and Map Change Update for the High Definition Map-Based Autonomous Driving Car,” Sensors, vol. 18, no. 9, pp. 3145, Sep. 2018.
GNSS/INS commercial post-processing tool - Inertial Explorer® [Online]. Available: https://novatel.com/products/waypoint-post-processing-software/inertial-explorer (accessed on 25 Apr 2023).
Introduction of Kalman filter [Online], Available: https://en.wikipedia.org/wiki/Kalman_filter (accessed on 25 Apr 2023).
Velodyne Lidar [Online]. Available: https://velodynelidar.com/ (accessed on 22 Sep 2021).
Lidar Calibration tool: TerraMatch [Online]. Available: https://terrasolid.com/ (accessed on 22 Sep 2021).
CalibrationToolkit instruction video [Online]. Available: https://www.youtube.com/watch?v=pfBmfgHf6zg (accessed on 18 Jun 2022).
NVIDIA DriveWorks SDK [Online], Available: https://developer.nvidia.com/drive/driveworks (accessed on 22 Sep 2021).
Hutoushan Innovation hub. Available online: https://www.hutoushan-innohub.org.tw/ (accessed on 1 May 2020)
C. -W. Peng, C. -C. Hsu and W. -Y. Wang, "Mobile Mapping System for Automatic Extraction of Geodetic Coordinates for Traffic Signs Based on Enhanced Point Cloud Reconstruction," in IEEE Access, vol. 10, pp. 117374-117384, 2022, doi: 10.1109/ACCESS.2022.3219415.