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A State-of-the-Art Review on SLAM

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Book cover Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13457))

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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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References

  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)

    Google Scholar 

  2. Bajcsy, R.: Active perception. Proc. IEEE 76(8), 966–1005 (1988)

    Article  Google Scholar 

  3. Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying framework. Int. J. Comput. Vision 56(3), 221–255 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  8. Castellanos, J.A., Neira, J., Tardos, J.D.: Limits to the consistency of EKF-based slam. In: Symposium on Intelligent Autonomous Vehicles (2004)

    Google Scholar 

  9. Chen, X., Läbe, T., Milioto, A., Röhling, T., Behley, J., Stachniss, C.: OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization. Auton. Robots 46, 61–81 (2021). https://doi.org/10.1007/s10514-021-09999-0

    Article  Google Scholar 

  10. Civera, J., Davison, A.J., Montiel, J.: Inverse depth parametrization for monocular SLAM. IEEE Trans. Rob. 24(5), 932–945 (2008)

    Article  Google Scholar 

  11. Debeunne, C., Vivet, D.: A review of visual-lidar fusion based simultaneous localization and mapping. Sensors 20(7), 2068 (2020)

    Article  Google Scholar 

  12. Durrant-Whyte, H., Bailey, T.: Simultaneous localisation and mapping (SLAM): part 2. IEEE Robot. Autom. Mag. 13(3), 108–117 (2006)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  15. Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: On-manifold preintegration theory for fast and accurate visual-inertial navigation. Comput. Sci. (2015)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Ganti, P., Waslander, S.L.: Visual slam with network uncertainty informed feature selection (2018)

    Google Scholar 

  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)

    Article  Google Scholar 

  20. Hartley, R.I., Zisserman, A.: Multi-view geometry in computer vision. Kybernetes 30(9/10), 1865–1872 (2019)

    Google Scholar 

  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)

    Google Scholar 

  22. Hong, S., Ko, H., Kim, J.: VICP: velocity updating iterative closest point algorithm. In: IEEE International Conference on Robotics and Automation (2012)

    Google Scholar 

  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)

    Article  Google Scholar 

  24. Huang, G.: Visual-inertial navigation: a concise review. In: 2019 International Conference on Robotics and Automation (ICRA) (2019)

    Google Scholar 

  25. Zhang, J., Singh, S.: Low-drift and real-time lidar odometry and mapping. Auton. Robot. 41(2), 401–416 (2016). https://doi.org/10.1007/s10514-016-9548-2

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  32. Lianos, K.N., Schnberger, J.L., Pollefeys, M., Sattler, T.: VSO: visual semantic odometry. In: European Conference on Computer Vision (ECCV) (2018)

    Google Scholar 

  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)

    Google Scholar 

  34. Lourakis, M.: SBA: a software package for generic sparse bundle adjustment. ACM Trans. Math. Softw. 36(1), 2 (2009)

    Article  MathSciNet  Google Scholar 

  35. Lupton, T., Sukkarieh, S.: Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions. IEEE Press (2012)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  40. Scaramuzza, D., Fraundorfer, F.: Visual odometry [tutorial]. IEEE Robot. Autom. Mag. 18(4), 80–92 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  43. Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics, pp. 435–461 (1988)

    Google Scholar 

  44. Stenborg, E., Toft, C., Hammarstrand, L.: Long-term visual localization using semantically segmented images, pp. 6484–6490 (2018)

    Google Scholar 

  45. Sucar, E., Hayet, J.B.: Bayesian scale estimation for monocular slam based on generic object detection for correcting scale drift (2017)

    Google Scholar 

  46. Sucar, E., Liu, S., Ortiz, J., Davison, A.J.: iMAP: implicit mapping and positioning in real-time (2021)

    Google Scholar 

  47. Thrun, S.: Probabilistic robotics. Commun. ACM 45(3), 52–57 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  50. Zhao, M., et al.: A general framework for lifelong localization and mapping in changing environment. arXiv e-prints (2021)

    Google Scholar 

  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)

    Google Scholar 

  52. Zhu, Z., et al.: NICE-SLAM: neural implicit scalable encoding for slam (2021)

    Google Scholar 

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Acknowledgment

This work was funded by the National Natural Science Foundation of China (No. 51975155), the Natural Science Foundation of Guangdong Province (No. 2021A1515011823) and the Shenzhen Basic Research Program (No. JCYJ202008-24082533001).

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China

    Xuewei Zhou & Ruining Huang

Authors
  1. Xuewei Zhou
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  2. Ruining Huang
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Corresponding author

Correspondence to Ruining Huang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Harbin, China

    Weihong Ren

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Zhou, X., Huang, R. (2022). A State-of-the-Art Review on SLAM. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_22

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13834-8

  • Online ISBN: 978-3-031-13835-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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