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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21384完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李明穗 | |
| dc.contributor.author | Chun-Yi Yu | en |
| dc.contributor.author | 余俊毅 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:32:37Z | - |
| dc.date.copyright | 2019-08-15 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-08 | |
| dc.identifier.citation | Raúl Mur-Artal and Juan D. Tardós, “ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras,” IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, 2017.
Berta Bescos, José M. Fácil, Javier Civera, and José Neira, “DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 4076-4083, 2018. Christian Kerl, Jürgen Sturm, and Daniel Cremers, “Dense Visual SLAM for RGB-D Cameras,” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2013. Jakob Engel, Thomas Schöps, and Daniel Cremers, “LSD-SLAM: Large-Scale Direct Monocular SLAM,” European conference on computer vision, Springer, Cham, 2014. Jakob Engel, Vladlen Koltun, and Daniel Cremers, “Direct Sparse Odometry,” IEEE transactions on pattern analysis and machine intelligence, vol. 40, no. 3, pp. 611-625, 2017. Rui Wang, Martin Schwörer, and Daniel Cremers, “Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras,” Proceedings of the IEEE International Conference on Computer Vision, 2017. Waleed Abdulla, “Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow,” https://engineering.matterport.com/splash-of-color-instance-segmentation-with-mask-r-cnn-and-tensorflow-7c761e238b46, 2018. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg, “SSD: Single Shot Multibox Detector,” European conference on computer vision. Springer, Cham, 2016. Joseph Redmon and Ali Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767, 2018. Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam, “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” Proceedings of the European conference on computer vision (ECCV), 2018. Jifeng Dai, Kaiming He, and Jian Sun, “Instance-aware Semantic Segmentation via Multi-Task Network Cascades,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick, “Mask R-CNN,” Proceedings of the IEEE international conference on computer vision, 2017. Ioan Andrei Bârsan, Peidong Liu, Marc Pollefeys, and Andreas Geiger, “Robust Dense Mapping for Large-Scale Dynamic Environments,” 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2018. Olaf Kähler, Victor Adrian Prisacariu, Carl Yuheng Ren, Xin Sun, Philip Torr, and David Murray, “Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices,” IEEE transactions on visualization and computer graphics, vol. 21, no.11, pp. 1241-1250, 2015. Linhui Xiao, Jinge Wang, Xiaosong Qiu, Zheng Rong, and Xudong Zou, “Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment,” Robotics and Autonomous Systems, vol. 117, pp. 1-16, 2019. TWICE, “TWICE ‘YES or YES’ Dance Video,” https://www.youtube.com/watch?v=Nl4BJ2TDmWE, 2018. Raúl Mur-Artal, J. M. M. Montiel, and Juan D. Tardós, “ORB-SLAM: A Versatile and Accurate Monocular SLAM System,” IEEE transactions on robotics, vol. 31, no. 5, pp. 1147-1163, 2015. Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers, “A Benchmark for the Evaluation of RGB-D SLAM Systems,” 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2012. Andreas Geiger, Philip Lenz, and Raquel Urtasun, “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite,” 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2012. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21384 | - |
| dc.description.abstract | 一般來說，同步定位與地圖構建（SLAM）演算法都假設是在靜態環境下運行，然而在現實世界中往往充滿行進的車輛與走動的人。如此嚴格的假設限制了其可用性，尤其是在機器人或自動駕駛車輛上進行。
在本篇論文，我們提出一個具備語義的視覺定位系統，能夠使用深度或立體相機，在高度動態的場景進行相機自我定位。本系統基於ORB-SLAM2，搭配目前表現佳且快速的物體偵測方法取得語義資訊。我們運用兩個公開的資料集來評估系統表現。此系統計算比DynaSLAM快，同時達到相近的定位準確度。對運算時間的分析也一併呈現在論文中。 | zh_TW |
| dc.description.abstract | Typically, simultaneous localization and mapping (SLAM) algorithms are assumed to be performed in the stationary environments only. However, there are ordinarily moving cars and people in the real world. Such a strict assumption restricts its usability especially on the robots or autonomous vehicles.
In this work, we present a semantic SLAM for RGB-D and stereo cameras, which can deal with highly dynamic scenes. The visual SLAM system is built on ORB-SLAM2 and the semantic information is acquired from state-of-the-art object detection with high frame rate. Experiments are conducted on two public benchmarks. Our system is faster than DynaSLAM and keeps the similar localization accuracy. The analysis of the computation time is also presented. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:32:37Z (GMT). No. of bitstreams: 1 ntu-108-R06944004-1.pdf: 3187070 bytes, checksum: 2882253c5b1fe8ffa573ae577423d2f9 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Types of Cameras in SLAM 2 Chapter 2 Related Work 4 2.1 SLAM 4 2.2 Object Detection and Segmentation 5 2.3 Semantic SLAM 6 Chapter 3 System Design 9 3.1 System Overview 9 3.2 Tracking 10 3.2.1 Semantic Input Preprocessing 10 3.2.2 Pose Prediction or Relocalization 14 3.2.3 Local Map Tracking (Motion-only Bundle Adjustment) 14 3.3 Local Mapping 14 3.3.1 Keyframe Insertion 14 3.3.2 Map Point Management 15 3.3.3 Local Bundle Adjustment 15 3.3.4 Local Keyframe Culling 15 3.4 Loop Closing and Full Bundle Adjustment 16 3.4.1 Loop Detection and Correction 16 3.4.2 Full Bundle Adjustment 16 Chapter 4 Experiments 17 4.1 TUM RGB-D Benchmark 17 4.2 KITTI Odometry Benchmark 23 4.3 Computation Time 26 Chapter 5 Conclusion 28 Chapter 6 Future Work 29 REFERENCES 31 | |
| dc.language.iso | en | |
| dc.title | 在動態環境使用相機進行具語義之同步定位與地圖構建 | zh_TW |
| dc.title | Semantic SLAM for Dynamic Environments Using Cameras | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 洪一平 | |
| dc.contributor.oralexamcommittee | 陳祝嵩,陳冠文 | |
| dc.subject.keyword | 同步定位與地圖構建,具語義之同步定位與地圖構建,深度相機,立體相機,物體偵測,動態環境, | zh_TW |
| dc.subject.keyword | simultaneous localization and mapping (SLAM),semantic SLAM,RGB-D camera,stereo camera,object detection,dynamic environment, | en |
| dc.relation.page | 33 | |
| dc.identifier.doi | 10.6342/NTU201902845 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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|---|---|---|---|
| ntu-108-1.pdf 未授權公開取用 | 3.11 MB | Adobe PDF |
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