請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王傑智(Chieh-Chih Wang) | |
| dc.contributor.author | Andreas Dopfer | en |
| dc.contributor.author | 竇菲 | zh_TW |
| dc.date.accessioned | 2021-05-20T20:06:29Z | - |
| dc.date.available | 2009-08-12 | |
| dc.date.available | 2021-05-20T20:06:29Z | - |
| dc.date.copyright | 2009-08-12 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-08-11 | |
| dc.identifier.citation | Besl, P. & McKay, H. A method for registration of 3-d shapes. IEEE Transactions on Pattern
Analysis and Machine Intelligenc. Burgard, W., Fox, D., Jans, H., Matenar, C., & Thrun, S. (1999). Sonar-based mapping of large-scale mobile robot environments using em. International Conference on Machine Learning. Cheeseman, P. & Smith, R. (1986). On the representation and estimation of spatial uncer- tainty. International Journal of Robotics Research. Derenick, J., Miller, T., Spletzer, J., Kushleyev, A., Foote, T., Stewart, J., Bohren, A., & Lee, D. (2009). The sick lidar matlab/c++ toolbox: Software for rapidly interfacing/configuring sick lidars with applications to real-time experimental robotics. IEEE/RSJ International Conference on Intelligent Robots and Systems. Elfes, A. (1987). Sonar-based real-world mapping and navigation. IEEE Transactions on Robotics and Automation. Frueh, C. & Zakhor, A. (2004). An automated method for large-scale, ground-based city model aquisition. International Journal on Computer Vision. Harrison, A. & Newman, P. (2008). High quality 3d laser ranging under general vehicle motion. IEEE International Conference on Robotics and Automation, 7–12. Li, G. & Liu, Y. (2007). An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features. International Conference on Intelligent Robots and Systems. Lu, F. & Milios, E. (1997). Robot pose estimation in unknown environments by matching 2d range scans. Journal of Intelligent and Robotic Systems, 18(3), 249275. Martin, M. & Moravec, H. (1996). Robot evidence grids. Technical report, Carnegie Mellon University. Nuechter, A., Lingemann, K., Hertzberg, K., & Surman, H. (2005). 6d slam with approxi- mate data association. International Conference on Advanced Robotics. Pless, R. & Zhang, Q. (2004). Extrinsic calibration of a camera and laser range finder. Inter- national Conference on Intelligent Robots and Systems. Rusinkiewicz, S. & Levoy, M. (2001). Efficient variants of the icp algorithm. Technical report, Stanford University. Ryde, J. & Hu, H. (2007). Mobile robot 3d perception and mapping with multi-resolution occupancy lists. IEEE International Conference on Mechatronics and Automation. Schulz, W. H. (2007). Landslide susceptibility revealed by lidar imagery and historical records, seattle, washington. Engineering Geology, 89(1-2), 67 – 87. Thrun, S., Burgard, W., & Fox, D. (2000). A real time algorithm for mobile robot map- ping with applications to multi robot and 3d mapping. IEEE International Conference on Robotics and Automation. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. Cambridge, Massachusetts: The MIT Press. Thrun, S., Ghahramini, Z., Koller, D., Durrant-Whyte, H., & Ng, A. (2002). Simultaneous mapping and localization with sparse extended information filters. Proceedings of the Fifth International Workshop on Algorithic Foundations of Robotics. Thrun, S., Haehnel, D., Burgard, W., Ferguson, D., Montemerlo, M., Triebel, R., Baker, C., & Whittaker, W. (2003). A system for volumetric robotic mining in abandoned mines. IEEE International Conference on Robotics and Automation. Wulf, O. & Wagner, B. (2003). Fast 3d scanning methods for laser measurement systems. Technical report, Institute for Systems Engineering, University of Hannover, Germany. Zhao, H., Chen, Y., & Shibasaki, R. (2007). An efficient extrinsic calibration of a multiple laser scanners and cameras’ sensor system on a mobile platform. IEEE Inteligent Vehicle Symposium. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9009 | - |
| dc.description.abstract | 在這篇論文中，我們提出一種只利用一台二維LIDAR讓機器人在三維空間中做定位和建立三維空間資訊的方法。我們同時利用事前的環境知識和機器人本人的運動來完成這件事。我們主要是達成一種在取得三維空間資訊的同時，也提供了比得上使用水平二維LIDAR定位效能的方法 | zh_TW |
| dc.description.abstract | Much work on localization and mapping using LIDAR has been done in mobile robotics. While earlier work was done only in the two dimensional domain, a recent shift towards three dimensional localization and mapping using laser rangefinder can be seen. Three dimensional representations allow a more accurate modeling of the real world, allowing more sophisticated path planning and leading to better obstacle avoidance. Also the performance of localization can be improved,and three dimensional data allows better object recognition than 2D data.
Techniques capturing 3D data involve either multiple 2D LIDARS, one 2D LIDAR that is nodded or rotated using an external actuator together with highly accurate orientation sensing and synchronization, or an integrated, expensive 3D scanning system. In this thesis we propose a technique to capture 3D data only using one 2D LIDAR. To do so the robots motion is utilized together with reasonable assumptions. It is assumed that the ground the robot is moving on is flat and visible in the scan, that the sensors height is known and that the environment has vertical structures. First an initial calibration procedure using a camera together with the LIDAR is performed to reveal the extrinsic parameters between robot and the sensor. The localization problem is divided into two steps. The LIDARs sensing plane is tilted away from the robots direction of motion towards the floor (or another known flat structure in the environment). The detection of the floor allows to estimate the angular orientation of the sensor in two dimensions. Using these estimates the range data can be transformed, so that known methods to estimate the missing parameters of the full LIDAR pose can be adopted. Being able to accurately estimate the three dimensional displacement between two consecutive scans allows to build an accurate three dimensional map of the environment. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T20:06:29Z (GMT). No. of bitstreams: 1 ntu-98-R96922144-1.pdf: 18713904 bytes, checksum: 94714976409801a758bfc531e378b3d9 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v CHAPTER 1. Introduction . 1 Localization . . . . . . . . 1 Mapping . . . . . . . . . . 2 Thesis objective . . . . . . 2 Overview . . . . . . . . . 2 CHAPTER 2. Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1. Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2. Mapping in 3D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 CHAPTER 3. Foundations . . . . . . . . . . . . . . . . . . 6 3.1. LIDAR . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2. Raw Scan Processing . . . . . . . . . . . . . . . . . 8 3.2.1. Linear Least Square Line fitting . . . . . . . . 8 3.2.2. Line splitting . . . . . . . . . . . . . . . . . . . 8 3.3. Iterative Closest Point (ICP) Algorithm . . . . . . . 10 3.3.1. Improvements over the Naive Implementation 11 3.4. Clustering . . . . . . . . . . . . . . . . . . . . . . . 12 3.5. LIDAR calibration . . . . . . . . . . . . . . . . . . 13 3.5.1. Extrinsic Camera - LIDAR calibration . . . . . 13 3.5.2. Calibrating the LIDAR’s position . . . . . . . 17 CHAPTER 4. 3D Localization and Mapping using one 2D LIDAR 18 4.1. Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2. Estimating the Orientation . . . . . . . . . . . . . . . . . . . 20 4.2.1. Identifying the ground line . . . . . . . . . . . . . . . . 20 4.2.2. Selection Issues . . . . . . . . . . . . . . . . . . . . . . 21 4.2.3. Calculating Pitch and Roll . . . . . . . . . . . . . . . . 22 4.3. Estimating the Robot Motion . . . . . . . . . . . . . . . . . 23 4.3.1. Scan Projection . . . . . . . . . . . . . . . . . . . . . . . 23 4.3.2. Scan Matching Challenges . . . . . . . . . . . . . . . . 24 4.3.3. Constant Velocity Motion Model . . . . . . . . . . . . . 26 4.3.4. Sampling based Approach . . . . . . . . . . . . . . . . 26 4.4. Uncertainty Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.4.1. Uncertainty Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 CHAPTER 5. Experimental Results . . . . . . . . . 30 5.1. Hardware . . . . . . . . . . . . . . . . . . . 30 5.1.1. Extrinsic camera-LIDAR calibration . . 32 5.1.2. LIDAR calibration . . . . . . . . . . . . 32 5.2. Software Implementation . . . . . . . . . . 33 5.3. 3D-map of the CSIE Building: Fourth floor . 33 5.4. 3D-map of the CSIE Building: First floor . . 38 5.4.1. Performance . . . . . . . . . . . . . . . 42 5.4.2. Issues . . . . . . . . . . . . . . . . . . . 42 5.5. Gridmaps . . . . . . . . . . . . . . . . . . . 44 CHAPTER 6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.1. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 | |
| dc.language.iso | en | |
| dc.title | 運用單具二維LIDAR在立體環境中定位與建地圖 | zh_TW |
| dc.title | 3D Localization and Mapping Using One 2D LIDAR | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃漢邦(Han-Pang Huang),林達德(Ta-Te Lin),康仕仲(Shih-Chung Kang) | |
| dc.subject.keyword | 建立三維空間, | zh_TW |
| dc.subject.keyword | 3D mapping,LIDAR, | en |
| dc.relation.page | 48 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2009-08-11 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-98-1.pdf | 18.28 MB | Adobe PDF | 檢視/開啟 |
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