基於語義型閉環檢測之機器人同步建圖與定位系統

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姓名

楊哲宇(Che-Yu Yang) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於語義型閉環檢測之機器人同步建圖與定位系統
(Toward Semantic Loop Closure in Simultaneous Localization and Mapping Systems)

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摘要(中)

即時定位與建圖系統SLAM(Simultaneous localization and mapping)是用來幫助機器人建立環境資訊同時估計自身的位姿的一種技術，被用來解決很多的新興應用，像是自駕車、無人載具、家庭式服務機器人等。可想而知，SLAM系統的精確度受到其光學設備影響非常的大，像是雷射、彩色相機、聲納等，皆會有不同的輸入訊號品質，進而影響著SLAM系統的效能。SLAM系統中很重要的一樣就是閉環檢測，其主要的功能是讓機器人識別出過去曾經到過的場景，來減小系統的累積誤差。在傳統的SLAM系統中，主要是利用幾何特徵來做識別，但是在有非常相似幾何特徵的場景中，其效果會下降非常的多。因此需要新的環境特徵來增加其效果，語義型物件特徵，即被用來當作全新的環境特徵，給予SLAM系統一個新種類的環境資訊來做識別。在此論文中，我們首先會先對SLAM系統做個介紹，再針對語義型物件方法提升分辨相似場景的能力做介紹。在室內環境中，有非常多的幾何上相似的場景出現，因此語義型物件特徵可以提供比幾何特徵更好的效果。

摘要(英)

Simultaneous localization and mapping (SLAM) is a problem in robotics aiming to model the environment and estimate the pose of a device within it at the same time. Developed solution is the core technology for emerging applications such as self-driving cars, automated guided vehicles (AGV), and domestic robots. Inevitably, the performance of SLAM algorithms relies highly on input signals from optical equipment ranging from cameras, laser rangefinders, and LIDAR. Loop closure, the function detecting visited locations to correct accumulated errors, is a crucial element in a SLAM system. Conventionally, geometric features are used to interpret the scenes for similarity estimation. In scenarios with nearly identical scenes existing, the feature-based approaches remain ineffective. Semantic objects, therefore, can be integrated into the process and present a new level of environmental information. In this article, we first provide an overview of the SLAM system. Then a semantic object-assisted approach is proposed to improve the similarity measurement in the SLAM process. By integrating recognized objects like landmarks and signs, we can classify similar scenes better and significantly improve building-scale indoor mapping results.

關鍵字(中)

★ 環境辨識
★ 閉環檢測
★ 物件辨識

關鍵字(英)

★ Place Recognition
★ Bag of words approach
★ Appearance-based localization and mapping
★ SLAM

論文目次

Table of Contents
1 Introduction
1.1 Background.................................. 1
1.2 Motivation................................... 2
1.3 Contribution.................................. 2
1.4 Framework.................................. 3
2 Background of SLAM and Loop Closure......................4
2.1 Simultaneous Localization and Mapping (SLAM).............. 4
2.1.1 Sensor................................. 5
2.1.2 Visual Odometry,VO......................... 6
2.1.3 Optimization............................. 9
2.1.4 Mapping............................... 10
2.2 Loop Closure Detection............................ 11
2.2.1 Loop closure target.......................... 11
2.2.2 Basic method............................. 12
2.2.3 Precision and Recall......................... 13
2.2.4 Bag-of-Words(BoW)......................... 14
2.3 Related work................................. 16
3 System model....................................18
3.1 Design principle and architecture....................... 18
3.2 Single frame module............................. 20
3.3 Time and Spaital Sequences module..................... 22
3.4 Object detection module........................... 24
3.5 Probabilistic scoring module.........................25
3.5.1 Parameters setting..........................
3.5.2 The Score and Threshold Evaluation................. 26
4 Implementation.....................................28
4.1 Procedure................................... 28
4.2 Environment setting.............................. 30
4.3 Data processing................................ 31
4.3.1 Build the basic type to store information............... 31
4.3.2 Loading the data from database................... 32
4.4 Establish module............................... 33
4.4.1 Single frame module......................... 33
4.4.2 Time and Spatial sequences module................. 33
4.4.3 Object detection module....................... 35
4.4.4 Probabilistic scoring module..................... 36
4.5 Verificataion module............................. 37
5 PerformanceEvaluation...............................39
5.1 Realworldsituation.............................. 39
5.2 Singleframemethod............................. 40
5.3 Time and Spatial sequences module..................... 41
5.3.1 Compare to single frame method................... 42
5.3.2 ROC curve of time and spatial sequences method..........43
5.4 Probabilistic scoring module.........................43
6 Conclusion and Future Work.....................45
6.1 Conclusion.................................. 45
6.2 Futurework.................................. 45
bibliographystyle...................................46

參考文獻

references
[1] Kinect fusion:https://msdn.microsoft.com/en-us/library/dn188670.aspx.
[2] Dbow3 library:https://github.com/rmsalinas/dbow2,2017.
[3] Adrien Angeli,DavidFilliat,St’ephane Doncieux,andJean-ArcadyMeyer.Fastand incremental method for loop-closure detection using bags of visual words. IEEE Transactions on Robotics, 24(5):1027–1037,2008.
[4] David Arthur and Sergei Vassilvitskii. k-means++:The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027-1035.Society for Industrial and Applied Mathematics,2007.
[5] Herbert Bay,Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features.
In European conference on computer vision, pages 404–417. Springer,2006.
[6] Patrick Beeson,Joseph Modayil,and Benjamin Kuipers.Factoring the mapping problem: Mobile robot map building in the hybrid spatial semantic hierarchy. The International Journal of Robotics Research, 29(4):428–459,2010.
[7] Sean LBowman, Nikolay Atanasov, Kostas Daniilidis, and George J Pappas. Probabilistic data association for semantic slam.In Robotics and Automation(ICRA),2017
IEEE International Conference on, pages 1722–1729.IEEE,2017.
[8] C Chow and CongLiu. Approximating discrete probability distributions with dependence trees. IEEE transactions on Information Theory, 14(3):462–467,1968.
[9] Mark Cummins and Paul Newman.Fab-map:Probabilistic localization and mapping in the space of appearance. The International Journal of Robotics Research,
27(6):647–665, 2008.
[10] Mark Cummins and Paul Newman. Accelerating fab-map with concentration inequalities. IEEE Transactions on Robotics, 26(6):1042–1050,2010.
[11] Mark Cummins and Paul Newman. Appearance only slam at large scale with fab-map
2.0. The International Journal of Robotics Research, 30(9):1100–1123,2011.
[12] Hugh Durrant-Whyte and Tim Bailey. Simultaneous localization and mapping: part
i. IEEE robotics & automation magazine, 13(2):99-110,2006.
[13] Felix Endres, Jurgen Hess, Jurgen Sturm, Daniel Cremers, and Wolfram Burgard. 3d mapping with an rgb-d camera. IEEE Transactions on Robotics, 30(1):177–187,
2014.
[14] Dorian G’alvez-L’opez and Juan D Tardos. Bags of binary words for fast place recognition in image sequences. IEEE Transactions on Robotics, 28(5):1188–1197,2012.
[15] Xiao-Shan Gao,Xiao-RongHou,Jianliang Tang,and Hang-Fei Cheng. Complete solution classification for the perspective-three-point problem. IEEE transactions on
pattern analysis and machine intelligence, 25(8):930–943,2003
[16] Mathias Gehrig, Elena Stumm, Timo Hinzmann, and Rol and Siegwart. Visualplace recognition with probabilistic vertex voting. arXiv preprint arXiv:1610.03548, 2016.
[17] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. pages 580–587,2014.
[18] Dirk Hahnel, Wolfram Burgard, Dieter Fox, and Sebastian Thrun. An efficient fast-slam algorithm for generating maps of large-scale cyclic environments from raw laser range measurements.In Intelligent Robots and Systems,2003.(IROS2003).Proceedings. 2003 IEEE/RSJ International Conference on, volume 1,pages 206–211.IEEE,
2003.
[19] Ebrahim Karami, Siva Prasad, and Mohamed Shehata. Image matching using sift, surf, brief and orb: Performance comparison for distorted images. arXiv preprint arXiv:1710.02726, 2017.
[20] Mathieu Labb’e and Francois Michaud. Memory management for real-time appearance-based loop closure detection. In Intelligent Robots and Systems(IROS),
2011 IEEE/RSJ International Conference on, pages 1271-1276.IEEE,2011.
[21] Mathieu Labbe and Francois Michaud. Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Transactions on Robotics, 29(3):734–745, 2013.
[22] Mathieu Labb’e and Francois Michaud. Online global loop closure detection for large-scale multi-session graph-based slam. In Intelligent Robots and Systems (IROS2014), 2014 IEEE/RSJ International Conference on, pages 2661–2666.IEEE,2014.
[23] Mathieu Labb’e and Francois Michaud. Long-term online multi-session graph-based splam with memory management. Autonomous Robots, 42(6):1133–1150,Aug 2018.
[24] V.Lepetit, F.Moreno-Noguer, and P.Fua. Epnp: An accurate o(n) solution to the pnp problem. International Journal Computer Vision, 81(2), 2009.
[25]Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. SSD: Single shot multi box detector. pages 21–37. Springer,2016
[26] Stuart Lloyd. Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129–137,1982.
[27] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91–110,2004.
[28] Stephanie Lowry, Niko Sunderhauf,
Paul Newman,John J Leonard, David Cox, Peter
Corke,and Michael J Milford. Visual place recognition: A survey. IEEE Transactions on Robotics, 32(1):1–19,2016.
[29] Michael J Milford and Gordon F Wyeth. Seqslam: Visual route-based navigation for sunny summer days and stormy winter nights.In Robotics and Automation(ICRA),
2012 IEEE International Conference on, pages 1643–1649.IEEE,2012.
[30] Ra’ul Mur-Artal and Juan D Tard’os. Orb-slam2: an open-source slam system for monocular. Stereo and RGB-D Cameras. arXiv preprint, 2016.
[31] Paul Newman and KinHo. Slam-loop closing with visually salient features. In Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, pages 635–642.IEEE,2005.
[32] David Nister and Henrik Stewenius. Scalable recognition with a vocabulary tree. In Computer vision and pattern recognition, 2006 IEEE computer society conference on, volume 2, pages 2161–2168. IEEE,2006.
[33] Adrian Penate-Sanchez,Juan Andrade-Cetto, and Francesc Moreno-Noguer. Exhaustive linearization for robust camera pose and focal length estimation. IEEE
transactions on pattern analysis and machine intelligence, 35(10):2387–2400,2013.
[34] J.Redmon, S.Divvala, R.Girshick, and A.Farhadi. You only look once:Unified, real-time object detection. pages 779–788, June 2016.
[35] Joseph Redmon and Ali Farhadi. Yolo 9000: Better, faster, stronger. arXiv preprint arXiv:1612.08242, 2016.
[36] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv, 2018.
[37] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN:Towards real-time object detection with region proposal networks. pages 91–99,2015.
[38] Stephen Robertson. Under standing inverse document frequency: on theoretical arguments for idf. Journal of documentation, 60(5):503–520,2004.
[39] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In Computer Vision(ICCV),2011 IEEE international conference on, pages 2564–2571.IEEE,2011.
[40] Josef Sivic and Andrew Zisserman. Video google:
A text retrieval approach to object matching in videos. In null, page 1470. IEEE,2003.

指導教授

黃志煒(Chih-Wei Haung)

審核日期

2018-8-16

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