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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 連豊力(Feng-Li Lian) | |
| dc.contributor.author | Feng-Min Chang | en |
| dc.contributor.author | 張峰鳴 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:52:46Z | - |
| dc.date.available | 2017-08-17 | |
| dc.date.copyright | 2012-08-17 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-12 | |
| dc.identifier.citation | [1] D. Schulz, W. Burgard, D. Fox, and A. Cremers, “People tracking with mobile robots using sample-based joint probabilistic data association filters”, International Journal of Robotics Research, vol. 22, no. 2, pp. 99–116, 2003.
[2] M. Mucientes and W. Burgard, “Multiple Hypothesis Tracking of Clusters of People”, in Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 692-697, Beijing, P.R. China, Oct. 2006. [3] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics”, Editor: R. Arkin, London: The MIT Press, 2005. [4] E. Kiriy and M. Buehler, Three-state Extended Kalman Filter for Mobile Robot Localization, Technical Report, Electrical and Computer Engineering, McGill University, Montreal, 2002. [5] D. Wang, and C. B. Low, “Modeling and Analysis of Skidding and Slipping in Wheeled Mobile Robots: Control Design Perspective”, IEEE Transactions on Robotics, vol. 24, no. 3, pp. 676–687, Jun. 2008. [6] L. Kneip, F. Tˆache, G. Caprari, and R. Siegwart, “Characterization of the compact Hokuyo URG-04LX 2D laser range scanner”, in Proceedings of IEEE International Conference on Robotics and Automation, pp. 1447-1454, Kobe, Japan, May, 2009. [7] D. Wolf and G. Sukhatme. “Online Simultaneous Localization and Mapping in Dynamic Environments”, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1301-1307, New Orleans, LA, USA, Apr. 2004. [8] L. E. Navarro-Serment, C. Mertz, N. Vandapel, and M. Hebert, “LADAR-based Pedestrian Detection and Tracking”, in Proceedings of the IEEE ICRA the 1st Workshop on Human Detection from Mobile Robot Platforms, CA, USA, May, 2008. [9] T. D. Vu, O. Aycard, and N. Appenrodt, “Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments”, in Proceedings of IEEE Intelligent Vehicles Symposium, pp. 190-195, Istanbul, Turkey, Jun. 2007. [10] C. L. Chen, C. C. Chou, and F. L. Lian, “Detecting and Tracking of Host People on a Slave Mobile Robot for Service-Related Tasks”, in Proceedings of the SICE Annual Conference, pp. 1326-1331, Tokyo, Japan, Sept. 2011. [11] P. Besl and N. McKay, “A method for registration of 3d shape”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, 1992. [12] H. Moravec and A. Elfes, “High Resolution Maps from Wide Angle Sonar”, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 116-121, Missouri, USA, Mar. 1985. [13] D. Reid, “An algorithm for tracking multiple targets”, IEEE Transactions on Automatic Control, Vol. AC-24, No. 6, pp. 843–854, 1979. [14] T. Horiuchi, S. Thompson, S. Kagami, and Y. Ehara, “Pedestrian Tracking from a Mobile Robot using a Laser Range Finder”, in Proceedings of IEEE International Conference on Systems Man and Cybernetics, pp. 931-936, Montreal, Canada, Oct. 2007. [15] S. Thrun, “Learning occupancy grids with forward sensor models”, Autonomous Robots, vol. 15, pp. 111–127, 2003. [16] G. Oriolo, G. Ulivi, and M.Vindittelli, “Real-time map building and navigation for autonomous robots in unknown environments”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 28, no. 3, pp. 316–333, 1998. [17] S. Thrun, “Learning metric-topological maps for indoor mobile robot navigation”, Artificial Intelligence, vol. 99, no. 1, pp. 21–71, 1998. [18] J. Buhmann, W. Burgard, A. B. Cremers, D. Fox, T. Hofmann, F. Schneider, J. Strikos, and S. Thrun. “The mobile robot Rhino”, AI Magazine, vol. 16, no. 1, pp. 31–38, 1995. [19] D. Murray, J. Little, “Using real-time stereo vision for mobile robot navigation”, Autonomous Robots, vol. 8, no. 2, pp. 161–171, 2000. [20] W. Burgard, D. Fox, H. Jans, C. Matenar, and S. Thrun. “Sonar-based mapping of large-scale mobile robot environments using EM”, in Proceedings of IEEE International Conference on Machine Learning, pp. 67-76, Bled, Slovenia, Jun. 1999. [21] C. C. Wang, C. Thorpe, and S. Thrun, “Online Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects: Theory and Results from a Ground Vehicle in Crowded Urban Areas”, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 842-849, Taipei, Taiwan, Sept. 2003. [22] C. Coue, C. Pradalier, C. Laugier, Th. Fraichard, and P. Bessiere, 'Bayesian occupancy filtering for multitarget tracking: an automotive application', International Journal of Robotics Research, vol. 25, no. 1, pp. 19–30, 2006. [23] C. C. Wang, C. Thorpe, S. Thrun, M. Hebert, and H. Whyte, “Simultaneous Localization, Mapping and Moving Object Tracking”, The International Journal of Robotics Research, Vol. 26, No. 9, pp. 889-916, Sept. 2007. [24] K. Mekhnacha, Y. Mao, D. Raulo, and C. Laugier, “The “Fast Clustering-Tracking” algorithm in the Bayesian Occupancy Filter framework”, in Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 238-245, Seoul, Korea, Aug. 2008. [25] M. Bennewitz, W. Burgard, and S. Thrun, “Learning motion patterns of persons for mobile service robots”, in Proceedings of IEEE International Conference on Robotics and Automation, pp. 3601-3606, Washington D.C., USA, May, 2002. [26] E. A. Topp, H. I. Christensen, “Tracking for following and passing persons”, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and System, Edmonton, Alberta, Canada, Aug. 2005. [27] M. Bennewitz, W. Burgard, G. Cielniak, and S. Thrun, “Learning Motion Patterns of People for Compliant Robot Motion”, International Journal of Robotics Research, vol. 24, no. 1, pp. 31–48, 2005. [28] Y. Kida, S. Kagami, T. Nakata, M. Kouchi, and H. Mizoguchi, “Human Finding and Body Property Estimation by using Floor Segmentation and 3D Labelling”, in Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 2924-2929, Hague, Netherlands, Oct. 2004. [29] K. Okada, S. Kagami, M. Inaba, and H. Inoue, “Walking Human Avoidance and Detection from A Mobile Robot using 3D Depth Flow”, in Proceedings of IEEE International Conference on Robotics and Automation, pp. 2307-2312, Seoul, Korea, May, 2001. [30] X. Shao, H. Zhao, K. Nakamura, and K. Katabira, “Detection and Tracking of Multiple Pedestrians using Laser Range Finder”, in Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 2174-2179, San Diego, USA, Oct. 2007. [31] J. Lee, T. Tsubouchi, K. Yamamoto, and S. Egawa, “People Tracking Using a Robot in Motion with Laser Range Finder”, in Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 2936-2942, Beijing, P.R. China, Oct. 2006. [32] O. M. Mozos, R. Kurazume, and T. Hasegawa, “Multi-Part People Detection Using 2D Range Data”, International Journal of Social Robotics, vol. 2, no. 1, pp. 31–40, 2010. [33] D. Schulz, W. Burgard, D. Fox, and A. Cremers, “Tracking Multiple Moving Targets with a Mobile Robot using Particle Filters and Statistical Data Association”, in Proceedings of IEEE International Conference on Robotics and Automation, pp. 1665-1670, Seoul, Korea, Jul. 2001. [34] K. O. Arras, S. Grzonka, M. Luber, and W. Burgard, “Efficient People Tracking in Laser Range Data using a Multi-Hypothesis Leg-Tracker with Adaptive Occlusion Probabilities”, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1710-1715, Pasadena, CA, USA, May, 2008. [35] M. S. Ryoo and J. K. Aggarwal, “Observe-and-Explain: A New Approach for Multiple Hypotheses Tracking of Humans and Objects”, in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1-8, Anchorage, Alaska, USA, Jun. 2008. [36] J. Leonard and H. Durrant-Whyte, “Simultaneous map building and localization for an autonomous mobile robot”, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and System, pp. 1442-1447, Osaka, Japan, Nov. 1991. [37] P. Sermanet, R. Hadsell, M. Scoffier, U. Muller, and Y. LeCun, “Mapping and Planning under Uncertainty in Mobile Robots with Long-Range Perception”, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2525-2530, Nice, France, Sept. 2008. [38] H. Kawata, S. Kamimura, A. Ohya, J. Iijima, and S. Yuta, “Advanced functions of the scanning laser range sensor for environment recognition in mobile robots”, in Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 414-419, Heidelberg, Germany, Sept. 2006. [39] J. Pascoal, L. Marques, and A. T. de Almeida, “Assessment of laser range finders in risky environments”, in Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 692-697, Nice, France, Sept. 2008. [40] A. D. Ryer, “Light Measurement Handbook”, International Light, Inc., Newburyport, MA, USA, 1998. [41] Z. P. Yang, L. Ci, J. A. Bur, S. Y. Lin, and P. M. Ajayan, “Experimental observation of an extremely dark material made by a low-density nanotube array”, Nano Letters, vol. 8, no. 2, pp. 446–451, 2008. [42] Y. Bar-Shalom and X.-R. Li, Multitarget-Multisensor Tracking: Principles and Techniques, Storrs, USA, 1995. [43] I. J. Cox, “A Review of Statistical Data Association Techniques for Motion Correspondence”, International Journal of Computer Vision, vol. 10, no. 1, pp. 53–66, 1993. [44] I. J. Cox and S. L. Hingorani, “An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 2, pp. 138–150, 1996. [45] K. G. Murty, “An algorithm for ranking all the assignments in order of increasing cost”, Operations Research, vol. 16, pp. 682–687, 1968. [46] T. Kurien, Multitarget-Multisensor Tracking: Advanced Applications, Norwood, MA, USA, 1990. [47] Hokuyo Scanning Laser Range Finder URG-04LX-UG01 Specifications, Hokuyo Automatic Co., Ltd., Japan, Aug. 2009 [48] A. Petrovskaya and S. Thrun, “Model Based Vehicle Tracking in Urban Environments”, in Proceedings of IEEE International Conference on Robotics and Automation, Workshop on Safe Navigation, Zurich, Switzerland, 2009. [49] B. Lau, K. O. Arras, and W. Burgard, “Tracking Groups of People with a Multi-Model Hypothesis Tracker”, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3180-3185, Piscataway, NJ, USA, May, 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64530 | - |
| dc.description.abstract | Mobile robots manipulating in human environments with the information about where the target pedestrian is and in which direction the target pedestrian is heading typically can improve their interaction behaviors. By providing the capability of tracking the target pedestrian in an appropriate manner, the robot can assist people in various ways under different environments. In this thesis, a complete robot perception system which consists of robot localization, mapping, moving point detection, pedestrian detection, and target tracking for robust target pedestrian tracking in an unknown indoor dynamic environment is presented. To acquire robot position, a modified scan matching algorithm, called multi-scans ICP (MICP), is used by correcting raw odometry information. To map the environment, a novel polar grid based mapping approach is proposed to accomplish mapping task without suffering sensor information digitization problem, and the proposed polar grid mapping platform is able to handle the laser based sensor limitations which might critically diminish the mapping and moving objects detection results. To detect moving points under grid based system, a modified inverse observation model is proposed to overcome several frequently happened detection limitations which are not considered in the original framework. To detect pedestrian from moving points, three pedestrian extraction techniques are proposed to filter out less possible clusters. To track targets, the multiple hypothesis tracking (MHT) algorithm is chosen to deal with the data association problem for robust pedestrian tracking purpose. The key contribution of the thesis is the combination of polar grid based system and MHT which enhances the reliability and robustness of the pedestrian tracking especially when measurements are noisy.
Three laser range finder tests are performed to demonstrate the laser limitations. Besides, three pedestrian tracking experiments are designed to evaluate target tracking performance using different algorithm frameworks. The results compare and evaluate the effectiveness of each step in the proposed algorithm framework. In addition, two real-life pedestrian tracking scenarios are performed in order to show that the proposed system can be applied in real situation. The proposed robot perception system has been proved that it is still capable of tracking the target robustly when environmental and detecting noises are present. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:52:46Z (GMT). No. of bitstreams: 1 ntu-101-R99921087-1.pdf: 6864216 bytes, checksum: 842a0b7574d9a42d7d7ea6e5696bb736 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 摘要 I
ABSTRACT III TABLE OF CONTENTS VII LIST OF FIGURES XI LIST OF TABLES: XV CHAPTER 1 INTRODUCTION 1 1.1 MOTIVATION 2 1.2 PROBLEM FORMULATION 4 1.3 CONTRIBUTION 10 1.4 ORGANIZATION OF THE THESIS 11 CHAPTER 2 BACKGROUND AND LITERATURE REVIEW 13 2.1 OCCUPANCY GRID MAPPING 13 2.2 PEOPLE DETECTION 16 2.3 MULTIPLE HYPOTHESIS TRACKING 18 CHAPTER 3 ROBOT LOCALIZATION AND POLAR GRID BASED MAPPING UNDER LASER LIMITATION 21 3.1 ROBOT LOCALIZATION 24 3.1.1 Multi-Scans ICP 25 3.2 THE LIMITATION OF LASER RANGE FINDER 27 3.2.1 Effect of Laser Incident Angle 28 3.2.2 Effect of Target Surface Brightness 31 3.3 POLAR GRID BASED MAPPING 32 3.3.1 Grid Probability Update 33 3.3.2 Mapping through Intermediate Local Polar Grid Map 36 3.3.3 Polar Grid Based Data Preprocessing 42 CHAPTER 4 PEDESTRIAN DETECTION AND TRACKING 47 4.1 MOVING POINT DETECTION 49 4.1.1 Original Inverse Observation Model 51 4.1.2 Modified Inverse Observation Model 55 4.1.3 Moving Points Filtering 64 4.2 PEDESTRIAN DETECTION 67 4.2.1 Cluster Size 68 4.2.2 Spatial Constraint 69 4.2.3 Cluster Size Consistence 70 4.3 MULTIPLE HYPOTHESIS TRACKING 71 4.3.1 Kalman Filter-Based Target Track 73 4.3.2 Multiple Hypothesis Data Association 77 4.3.3 Pruning 81 CHAPTER 5 EXPERIMENTAL RESULTS & ANALYSIS 83 5.1 HARDWARE PLATFORM 85 5.1.1 ActiveMedia Pioneer 3 mobile robot 85 5.1.2 HOKUYO URG-04LX-UG01 Laser Range Finder 86 5.2 EXPERIMENTS OF LASER LIMITATIONS USING HOKUYO URG-04LX-UG01 LASER RANGE FINDER 88 5.2.1 Effect of Laser Incident Angle 89 5.2.2 Effect of Target Surface Brightness: 91 5.2.3 Moving Objects Detection under Laser Limitations 94 5.3 PRESET EXPERIMENTAL SCENARIOS 98 5.4 GLOBAL STATIC MAPPING RESULTS 101 5.4.1 Case 1: Pedestrian Tracking at Hallway 102 5.4.2 Case 2: Pedestrian Tracking at Hallway with Pillars 104 5.4.3 Case 3: Pedestrian Tracking under First Seen Problem 107 5.5 PEDESTRIAN TRACKING RESULTS 110 5.5.1 Case 1: Pedestrian Tracking at Hallway 110 5.5.2 Case 2: Pedestrian Tracking at Hallway with Pillars 114 5.5.3 Case 3: Pedestrian Tracking under First Seen Problem 117 5.6 PEDESTRIAN DETECTION PERFORMANCE EVALUATION 120 5.6.1 Case 1: Pedestrian Tracking at Hallway 120 5.6.2 Case 2: Pedestrian Tracking at Hallway with Pillars 123 5.6.3 Case 3: Pedestrian Tracking under First Seen Problem 126 5.7 TRACKING ACCURACY EVALUATION 127 5.7.1 Tracking Results 128 5.7.2 Trajectories Alignments 133 5.7.3 Results Analysis 135 5.8 REAL-LIFE EXPERIMENTAL SCENARIOS 138 5.8.1 Case 1: Single Pedestrian Tracking in Cluttered Hallway 138 5.8.2 Case 2: Two Pedestrians Tracking in Cluttered Hallway 147 CHAPTER 6 CONCLUSION & FUTURE WORK 157 REFERENCES 159 | |
| dc.language.iso | zh-TW | |
| dc.subject | 人類追蹤 | zh_TW |
| dc.subject | 極座標網格法 | zh_TW |
| dc.subject | 動態點偵測 | zh_TW |
| dc.subject | 人類辨識 | zh_TW |
| dc.subject | 多重假設追蹤演算法 | zh_TW |
| dc.subject | pedestrian detection | en |
| dc.subject | polar occupancy grid map | en |
| dc.subject | moving point detection | en |
| dc.subject | multiple hypothesis tracking algorithm | en |
| dc.subject | Pedestrian tracking | en |
| dc.title | 利用多重假設法基於極座標網格之室內行動機器人強健人類追蹤演算法 | zh_TW |
| dc.title | Polar Grid Based Robust Pedestrian Tracking with Indoor Mobile Robot using Multiple Hypothesis Tracking Algorithm | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 簡忠漢,李後燦,黃正民 | |
| dc.subject.keyword | 人類追蹤,極座標網格法,動態點偵測,人類辨識,多重假設追蹤演算法, | zh_TW |
| dc.subject.keyword | Pedestrian tracking,polar occupancy grid map,moving point detection,pedestrian detection,multiple hypothesis tracking algorithm, | en |
| dc.relation.page | 163 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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