製圖，以空間資訊為根本基礎，透過相機或雷射感測器等設備蒐集環境資訊，進而將所有空間資訊結合並重建環境，以達到空間資訊最大的應用。在製圖前要有能力判斷本身定位，方能確實將各個蒐集點所捕捉到的資訊做最適切的統整與拼接，定位成果的優劣牽涉到製圖精度的好壞，兩者可謂息息相關，本論文的氣墊船製圖亦由定位解的提升來奠定基礎。近年製圖技術多透過慣性導航系統(Inertial Navigation System, INS)以及全球導航定位系統(Global Navigation Satellite System, GNSS)之基礎於室外發展成移動式測繪及製圖系統(Mobile Mapping System, MMS)，然而室內環境受頻蔽效應影響未能以上述全球定位系統為基礎做定位，慣性導航系統不受訊號遮蔽等外力影響的特性則得以克服該環境，並承襲室外導航經驗所使用的移動式測繪及製圖系統的整合式概念，搭配即時定位及製圖技術(Simultaneous Localization and Mapping, SLAM)做應用，本論文即是以慣性導航系統輔以即時定位及製圖技術，透過時間同步的整合機制，作為發展氣墊船製圖系統的基礎依據和技術。
有鑑於地下管線圖資缺乏，以及當代社會型態對室內環境的依賴，本研究認為有必要發展適用於室內和地底較險惡環境的製圖系統以延伸導航的應用，除了室內導航的需求，為避免高雄氣爆類似的狀況發生，對於地下管線的監測提供完善製圖系統以及定位需求更是不可或缺，本論文提出以氣墊船為載具、雷射掃描儀搭配慣性感測元件(Inertial Measurement Unit, IMU)的整合式技術，以氣墊船水陸兩地皆能運行的特性來克服地下水道潮濕環境與崎嶇移動面，此狀態下的空間資訊蒐集勢必受震盪影響產生無可避免的雜訊，本論文亦針對此現象提出雜訊分離演算法(denoising algorithm)來因應於整合式導航應用，藉由頻域分析來瞭解氣墊船訊號在移動時產生的特性，進一步去蕪存菁保留真正本體移動的訊號區段，期望提升整合定位解的整體精度，以提供良好定位作為製圖應用的依據。雜訊分離演算法為本研究的具體貢獻，除了一般研究僅使用如ZUPT (Zero Update)和NHC(non-holonomic constraint)等特定的常用約制方法，本論文使用穩定小波轉換(Stationary Wavelet Transform, SWT)以彌補離散小波轉換(Discrete Wavelet Transform, DWT)在執行縮減取樣(downsampling)時導致的平移一致性(translation-invariant)缺失，用以作為進行雜訊分離之根本，針對氣墊船運動模式的分析並在慣性感測元件的原始解予以雜訊分離機制，求取更精確的真實運動訊號，可望延伸到各種整合訊號來分析其特性並達到定位精度更有效的提升。

Indoor navigation as well as pipeline detection has been under attention in recent years due to the device and technology developments with various requirements from urbanization. Thus, GNSS-denied environments are the targets to complete the mapping constructions or positioning applications. In this research, indoor and underground environments are detected and spatial information is acquired by laser scanner as further Simultaneous Localization and Mapping (SLAM) algorithm input. On the other hand, Inertial Navigation System (INS) is aided for improving position accuracy during scanning processes. The payload is mounted on either mobile robot or hovercraft in relative size of the regions based on the concept of Mobile Mapping System (MMS). This research provides the rigid payload for various platforms in related scenarios with INS as well as the laser scanner and the combination of the sensors’ data depends on the synchronization of receiving time by Kalman Filter (KF). The main focus emphasizes the capability of the payload on different platforms for further applications and the ability of the algorithm for accurate positioning so far by individual constraints. This research gives out the results from the robotic and vehicle MMS with the same payload in indoor areas to verify the improved integrated solution. Furthermore, the hovercraft is applied to validate the performance of underground mapping and positioning. Since hovercraft is a relatively distinctive platform for MMS, it would rely on INS results more due to the featureless situations unfavorable for the SLAM algorithm. Thus, the dataset of IMU raw data is additionally transferred to frequency domain for advance analysis. Spectrum analysis is implemented via Fast Fourier Transform (FFT), short-term Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT). The research consequently proposes the positional enhancement technique through the integrated solution with the employment of the denoising algorithm to the INS solution. Once the boost of the platform position is received, it can not only strengthen the mapping accuracy by feeding back the positional result but also broaden the usage in a greater scale such as underground positioning.

Andrews, Angus P. Kalman filtering: Theory and practice. Prentice-Hall, 1993.
Benson, Donald O. "A comparison of two approaches to pure-inertial and Doppler-inertial error analysis." IEEE Transactions on Aerospace and Electronic Systems 4 (1975): 447-455.
Bosse, Michael, Robert Zlot, and Paul Flick. "Zebedee: Design of a spring-mounted 3-d range sensor with application to mobile mapping." IEEE Transactions on Robotics 28.5 (2012): 1104-1119.
Brown, Robert Grover, and Patrick YC Hwang. Introduction to random signals and applied Kalman filtering. Vol. 3. New York: Wiley, 1992.
Brown, Robert Grover, and Patrick YC Hwang. Solutions Manual to Accompany Introduction to Random Signals and Applied Kalman Filtering. Wiley, 1992.
Bruton, A. M., K-P. Schwarz, and J. Škaloud. "The use of wavelets for the analysis and de-noising of kinematic geodetic measurements." Geodesy Beyond 2000. Springer, Berlin, Heidelberg, 2000. 227-232.
Caldecott, R., Poirier, M., Scofea, D., Svoboda, D. E., & Terzuoli, A. J. Underground mapping of utility lines using impulse radar. In IEE Proceedings F (Communications, Radar and Signal Processing) (Vol. 135, No. 4, pp. 343-353). IET Digital Library. (1988, August).
Chen, J., Kwong, K., Chang, D., Luk, J., & Bajcsy, R. Wearable sensors for reliable fall detection. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 3551-3554). IEEE. (2006, January).
Chiang, Kai-Wei. INS/GPS integration using neural networks for land vehicular navigation applications. No. NR-04589 UMI. Canada: University of Calgary, 2004.
Chiang, Kai-Wei, Thanh Trung Duong, and Jhen-Kai Liao. "The performance analysis of a real-time integrated INS/GPS vehicle navigation system with abnormal GPS measurement elimination." Sensors 13.8 (2013): 10599-10622.
Chiang, K. W., Tsai, G. J., Li, Y. H., & El-Sheimy, N. Development of LiDAR-based UAV system for environment reconstruction. IEEE Geoscience and Remote Sensing Letters, 14(10), 1790-1794. (2017).
Claire, T., G. Guillaume, and P. Mike. "High-End Gyroscopes, Accelerometers and IMUs for Defense, Aerospace & Industrial [Online], Yole Development, 2015."
Chu, C. H., Chiang, K. W., Liao, J. K., Rau, J. Y., Tseng, Y. H., Chen, J. H., & Chen, J. C. The performance of a tight INS/GNSS/photogrammetric integration scheme for land based MMS applications in GNSS denied environments. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, (39), 479. (2012).
Chu, C.H.; Chiang, K.W.; Lin, C.A. (2013): The performance analysis of a portable mobile mapping system with different gnss processing strategies, Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2013), Nashville, TN, September, 2013, pp 689 – 703
Daubechies, Ingrid. Ten lectures on wavelets. Vol. 61. Siam, 1992.
des Bouvrie, Bas. Improving rgbd indoor mapping with imu data. Diss. Master’s thesis, Delft University of Technology, 2011.
DeSouza, Guilherme N., and Avinash C. Kak. "Vision for mobile robot navigation: A survey." IEEE transactions on pattern analysis and machine intelligence 24.2 (2002): 237-267.
Einicke, G. A. Smoothing, Filtering and Prediction: Estimating the Past, Present and Future. Rijeka, Croatia: Intech. ISBN 978-953-307-752-9, 2012.
Ellum, C. M.: The Development of a Backpack Mobile Mapping System, Degree of Master, Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada. (2001)
El-Sheimy, Naser. The development of VISAT: a mobile survey system for GIS applications. University of Calgary, 1996.
El-Sheimy, Naser. "An overview of mobile mapping systems." FIG Working Week. 2005.
Eyre, Matthew, Andrew Wetherelt, and John Coggan. "Evaluation of automated underground mapping solutions for mining and civil engineering applications." Journal of Applied Remote Sensing 10.4 (2016): 046011.
Faragher, Ramsey. "Understanding the basis of the kalman filter via a simple and intuitive derivation [lecture notes]." IEEE Signal processing magazine 29.5 (2012): 128-132.
Ferland, F., Clavien, L., Frémy, J., Létourneau, D., Michaud, F., & Lauria, M. Teleoperation of AZIMUT-3, an omnidirectional non-holonomic platform with steerable wheels. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2515-2516). IEEE. (2010, October).
Gelb, Arthur, ed. Applied optimal estimation. MIT press, 1974.
Grewal, Mohinder S., Lawrence R. Weill, and Angus P. Andrews. Global positioning systems, inertial navigation, and integration. John Wiley & Sons, 2007.
Han, J. H., Park, C. H., Hong, C. K., & Kwon, J. H. Performance analysis of two-dimensional dead reckoning based on vehicle dynamic sensors during GNSS outages. Journal of Sensors, 2017. (2017).
Heinzel, Gerhard, Albrecht Rüdiger, and Roland Schilling. Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new at-top windows. 2002.
Khoshelham, Kourosh, and Sander Oude Elberink. "Accuracy and resolution of kinect depth data for indoor mapping applications." Sensors 12.2 (2012): 1437-1454.
Kohlbrecher, S., Von Stryk, O., Meyer, J., & Klingauf, U. A flexible and scalable slam system with full 3d motion estimation. In 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (pp. 155-160). IEEE. (2011, November).
Kouros, G., Kotavelis, I., Skartados, E., Giakoumis, D., Tzovaras, D., Simi, A., & Manacorda, G. 3D Underground Mapping with a Mobile Robot and a GPR Antenna. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3218-3224). IEEE. (2018, October).
Lee, J., Niko, D. L., Hwang, H., Park, M., & Kim, C. A GIS-based design for a smartphone disaster information service application. In 2011 First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering (pp. 338-341). IEEE. (2011, May).
Leonard, John J., and Hugh F. Durrant-Whyte. "Mobile robot localization by tracking geometric beacons." IEEE Transactions on robotics and Automation 7.3 (1991): 376-382.
Li, Shuai, Hubo Cai, and Vineet R. Kamat. "Uncertainty-aware geospatial system for mapping and visualizing underground utilities." Automation in Construction 53 (2015): 105-119.
Mascarich, F., Khattak, S., Papachristos, C., & Alexis, K. A multi-modal mapping unit for autonomous exploration and mapping of underground tunnels. In 2018 IEEE aerospace conference (pp. 1-7). IEEE. (2018, March).
Maybeck, Peter S. Stochastic models, estimation, and control. Vol. 3. Academic press, 1982.
McReynolds, Stephen Ralph. "Fixed interval smoothing-Revisited." Journal of Guidance, Control, and Dynamics 13.5 (1990): 913-921.
Mitri, S., Pervölz, K., Surmann, H., & Nüchter, A. Fast color-independent ball detection for mobile robots. In Proceedings of the IEEE International Conference Mechatronics and Robotics 2004 (MechRob'04) (No. CONF, pp. 900-905). (2004).
Montemerlo, M., Hähnel, D., Ferguson, D., Triebel, R., Burgard, W., Thayer, S., ... & Thrun, S. A system for three-dimensional robotic mapping of underground mines (No. CMU-CS-02-185). CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE. (2002).
Mostofi, Navid. 3D Indoor Mobile Mapping using Multi-Sensor Autonomous Robot. Diss. University of Calgary, 2015.
Müller, Meinard. Fundamentals of music processing: Audio, analysis, algorithms, applications. Springer, 2015.
Neumann, T., Ferrein, A., Kallweit, S., & Scholl, I. Towards a mobile mapping robot for underground mines. In Proceedings of the 2014 PRASA, RobMech and AfLaT International Joint Symposium, Cape Town, South Africa (pp. 27-28). (2014, November).
Nikoohemat, Shayan. Smart Campus Map. Diss. Doctoral dissertation, Technical University of Munich, 2013.
Niu, X., Zhang, H., Chiang, K. W., & El-Sheimy, N. Using land-vehicle steering constraint to improve the heading estimation of MEMS GPS/INS georeferencing systems. In Proc. Int. Conf. Canadian Geomatics (pp. 1-5). (2010, June).
North, E., Georgy, J., Tarbouchi, M., Iqbal, U., & Noureldin, A. Enhanced mobile robot outdoor localization using INS/GPS integration. In 2009 International Conference on Computer Engineering & Systems (pp. 127-132). IEEE. (2009, December).
Nüchter, Andreas, Jan Elseberg, and Dorit Borrmann. "Irma3D—An intelligent robot for mapping applications." IFAC Proceedings Volumes 46.29 (2013): 119-124.
Nüchter, A., Borrmann, D., Koch, P., Kühn, M., & May, S. A MAN-PORTABLE, IMU-FREE MOBILE MAPPING SYSTEM. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2. (2015).
Oliver, Michael, Bob Sapey, and Pam Thomas. Social work with disabled people. Palgrave Macmillan, 2012.
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. Journal of neuroengineering and rehabilitation, 9(1), 21. (2012).
Riisgaard, Søren, and Morten Rufus Blas. "SLAM for Dummies." A Tutorial Approach to Simultaneous Localization and Mapping22.1-127 (2003): 126.
Rudan, János, Zoltán Tuza, and Gábor Szederkényi. "Using LMS-100 laser rangefinder for indoor metric map building." Industrial Electronics (ISIE), 2010 IEEE International Symposium on. IEEE, 2010.
Scherzinger, Bruno M. "Inertial navigator error models for large heading uncertainty." Position Location and Navigation Symposium, 1996., IEEE 1996. IEEE, 1996.
Shin, Eun-Hwan. Accuarcy improvement of low cost INS/GPS for land applications. University of Calgary, 2001.
Shin, Eun-Hwan, and Naser El-Sheimy. "An unscented Kalman filter for in-motion alignment of low-cost IMUs." Position Location and Navigation Symposium, 2004. PLANS 2004. IEEE, 2004.
Shin, Eun-Hwan. "Estimation techniques for low-cost inertial navigation." UCGE report 20219 (2005).
Skaloud, Jan. Optimizing georeferencing of airborne survey systems by INS/DGPS. Calgary, 1999.
Surmann, H., Nüchter, A., Lingemann, K., & Hertzberg, J. 6D SLAM-preliminary report on closing the loop in six dimensions. IFAC Proceedings Volumes, 37(8), 197-202. (2004).
Surmann, H., Worst, R., Hennig, M., Lingemann, K., Nuechter, A., Pervoelz, K., ... & Hertzberg, J. Robocuprescue-robot league team kurt3d, germany. In Proceedings of the 2004 RoboCup Symposium. (2004, July).
Surmann, Hartmut, and Rainer Worst. "New applications with lightweight 3D-Sensors." VDI BERICHTE 1956 (2006): 171.
Titterton, David, John L. Weston, and John Weston. Strapdown inertial navigation technology. Vol. 17. IET, 2004.
Thrun, Sebastian. "Learning metric-topological maps for indoor mobile robot navigation." Artificial Intelligence 99.1 (1998): 21-71.
Woodman, Oliver J. An introduction to inertial navigation. No. UCAM-CL-TR-696. University of Cambridge, Computer Laboratory, 2007.
Zhang, Ji, and Sanjiv Singh. "LOAM: Lidar Odometry and Mapping in Real-time." Robotics: Science and Systems. Vol. 2. 2014.