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2022 | OriginalPaper | Chapter

A State-of-the-Art Review on SLAM

Authors : Xuewei Zhou, Ruining Huang

Published in: Intelligent Robotics and Applications

Publisher: Springer International Publishing

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Abstract

SLAM (Simultaneous Localization and Mapping), also known as CML (Concurrent Mapping and Localization), refers to real-time positioning and map building, or concurrent mapping and positioning. After nearly 30 years of research on SLAM, there have been quite a few breakthroughs in the SLAM community. This paper aims to provide an insightful review of information background, recent development, feature, implementation, and recent issue in SLAM. This paper includes the following parts: First of all, it gives an overview of the basic development of SLAM from its introduction to the present. Then, and most importantly, it summarizes the mainstream SLAM technology and theoretical basis. In addition, some cutting-edge and novel SLAM research results are discussed respectively. Finally, this paper summarizes and introduces some practical applications of SLAM technology.

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Literature
1.
Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M.: Pyramid methods in image processing. RCA Eng. 29(6), 33–41 (1983)
2.
3.
Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying framework. Int. J. Comput. Vision 56(3), 221–255 (2004)CrossRef
4.
Besl, P.J., Mckay, H.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)CrossRef
5.
Bowman, S.L., Atanasov, N., Daniilidis, K., Pappas, G.J.: Probabilistic data association for semantic slam. In: 2017 IEEE International Conference on Robotics and Automation (ICRA) (2017)
6.
Brasch, N., Bozic, A., Lallemand, J., Tombari, F.: Semantic monocular slam for highly dynamic environments. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018)
7.
Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Rob. 32(6), 1309–1332 (2016)CrossRef
8.
Castellanos, J.A., Neira, J., Tardos, J.D.: Limits to the consistency of EKF-based slam. In: Symposium on Intelligent Autonomous Vehicles (2004)
9.
10.
Civera, J., Davison, A.J., Montiel, J.: Inverse depth parametrization for monocular SLAM. IEEE Trans. Rob. 24(5), 932–945 (2008)CrossRef
11.
Debeunne, C., Vivet, D.: A review of visual-lidar fusion based simultaneous localization and mapping. Sensors 20(7), 2068 (2020)CrossRef
12.
Durrant-Whyte, H., Bailey, T.: Simultaneous localisation and mapping (SLAM): part 2. IEEE Robot. Autom. Mag. 13(3), 108–117 (2006)CrossRef
13.
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in Computer Vision, pp. 726–740 (1987)
14.
Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation (supplementary material). Georgia Institute of Technology (2015)
15.
Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: On-manifold preintegration theory for fast and accurate visual-inertial navigation. Comput. Sci. (2015)
16.
Frost, D., Prisacariu, V., Murray, D.: Recovering stable scale in monocular slam using object-supplemented bundle adjustment. IEEE Trans. Rob. 34, 736–747 (2018)CrossRef
17.
Galvez-Lpez, D., Tardos, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Rob. 28(5), 1188–1197 (2012)CrossRef
18.
Ganti, P., Waslander, S.L.: Visual slam with network uncertainty informed feature selection (2018)
19.
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Rob. 23(1), 34–46 (2007)CrossRef
20.
Hartley, R.I., Zisserman, A.: Multi-view geometry in computer vision. Kybernetes 30(9/10), 1865–1872 (2019)
21.
Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2D LIDAR SLAM. In: 2016 IEEE International Conference on Robotics and Automation (ICRA) (2016)
22.
Hong, S., Ko, H., Kim, J.: VICP: velocity updating iterative closest point algorithm. In: IEEE International Conference on Robotics and Automation (2012)
23.
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robot. 34(3), 189–206 (2013)CrossRef
24.
Huang, G.: Visual-inertial navigation: a concise review. In: 2019 International Conference on Robotics and Automation (ICRA) (2019)
25.
26.
Kim, G., Kim, A.: Scan context: egocentric spatial descriptor for place recognition within 3D point cloud map. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018)
27.
Kim, G., Kim, A.: Remove, then revert: static point cloud map construction using multiresolution range images. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020)
28.
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: IEEE and ACM International Symposium on Mixed and Augmented Reality (2008)
29.
Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2o: a general framework for graph optimization. In: IEEE International Conference on Robotics and Automation (2011)
30.
Leonard, J.J., Durrant-Whyte, H.F.: Simultaneous map building and localization for an autonomous mobile robot. In: Proceedings IROS 1991. IEEE/RSJ International Workshop on Intelligent Robots and Systems 1991. Intelligence for Mechanical Systems (1991)
31.
Liang, H.J., Sanket, N.J., Fermüller, C., Aloimonos, Y.: SalientDSO: bringing attention to direct sparse odometry. IEEE Trans. Autom. Sci. Eng. 16(4), 1619–1626 (2018)CrossRef
32.
Lianos, K.N., Schnberger, J.L., Pollefeys, M., Sattler, T.: VSO: visual semantic odometry. In: European Conference on Computer Vision (ECCV) (2018)
33.
Liu, H., Chen, M., Zhang, G., Bao, H., Bao, Y.: ICE-BA: incremental, consistent and efficient bundle adjustment for visual-inertial slam. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
34.
Lourakis, M.: SBA: a software package for generic sparse bundle adjustment. ACM Trans. Math. Softw. 36(1), 2 (2009)MathSciNetCrossRef
35.
Lupton, T., Sukkarieh, S.: Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions. IEEE Press (2012)
36.
Martinezmontiel, J.: Scale drift-aware large scale monocular SLAM. In: Robotics: Science and Systems VI, Universidad de Zaragoza, Zaragoza, Spain, 27–30 June 2010 (2010)
37.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ren, N.: NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99–106 (2022)CrossRef
38.
Mourikis, A.I., Roumeliotis, S.I.: A multi-state constraint Kalman filter for vision-aided inertial navigation. In: 2007 IEEE International Conference on Robotics and Automation (2007)
39.
Saputra, M.R.U., Markham, A., Trigoni, N.: Visual slam and structure from motion in dynamic environments: a survey. ACM Comput. Surv. (CSUR) 51(2), 1–36 (2018)CrossRef
40.
Scaramuzza, D., Fraundorfer, F.: Visual odometry [tutorial]. IEEE Robot. Autom. Mag. 18(4), 80–92 (2011)CrossRef
41.
Schauer, J., Nuechter, A.: The peopleremover-removing dynamic objects from 3-D point cloud data by traversing a voxel occupancy grid. IEEE Robot. Autom. Lett. 3(3), 1679–1686 (2018)CrossRef
42.
Serviéres, M., Renaudin, V., Dupuis, A., Antigny, N.: Visual and visual-inertial slam: state of the art, classification, and experimental benchmarking. J. Sens. 2021(1), 1–26 (2021)CrossRef
43.
Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics, pp. 435–461 (1988)
44.
Stenborg, E., Toft, C., Hammarstrand, L.: Long-term visual localization using semantically segmented images, pp. 6484–6490 (2018)
45.
Sucar, E., Hayet, J.B.: Bayesian scale estimation for monocular slam based on generic object detection for correcting scale drift (2017)
46.
Sucar, E., Liu, S., Ortiz, J., Davison, A.J.: iMAP: implicit mapping and positioning in real-time (2021)
47.
48.
Qin, T., Li, P., Shen, S.: VINS-mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans. Robot. 34(4), 1004–1020 (2018)CrossRef
49.
Yu, C., et al.: DS-SLAM: a semantic visual slam towards dynamic environments. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018)
50.
Zhao, M., et al.: A general framework for lifelong localization and mapping in changing environment. arXiv e-prints (2021)
51.
Zhong, F., Sheng, W., Zhang, Z., Chen, C., Wang, Y.: Detect-slam: making object detection and slam mutually beneficial. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (2018)
52.
Zhu, Z., et al.: NICE-SLAM: neural implicit scalable encoding for slam (2021)
Metadata
Title
A State-of-the-Art Review on SLAM
Authors
Xuewei Zhou
Ruining Huang
Copyright Year
2022
Book
Intelligent Robotics and Applications

Print ISBN: 978-3-031-13834-8

Electronic ISBN: 978-3-031-13835-5

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-13835-5_22

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