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New Trend in Back-End Techniques of Visual SLAM: From Local Iterative Solvers to Robust Global Optimization

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Artificial Intelligence in China

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 854))

Abstract

Visual simultaneous localization and mapping (V-SLAM) technique plays a key role in perception of autonomous mobile robots, augmented/mixed/virtual reality, as well as high-level geometric perception and spatial AI applications. This paper gives a very concise survey about the back-end module of a V-SLAM system, which is essentially a nonlinear least square (NLLS) problem. This problem is traditionally solved by a local iterative linearized optimization algorithm with a good initial guess, such as the Gauss-Newton algorithm. Due to the nonconvexity of the NLLS problem, these local iterative solvers cannot provide any guarantee on the global minimum convergence, which is crucial for active SLAM systems and life-critical applications, such as autonomous driving. Therefore, new trend about robust global optimization algorithms for the pose graph are introduced, which adopt duality theory, convex relaxation, and robust cost functions to provide a certified global optimal solution.

This work was supported by the Pre-Research Project of Space Science (No. XDA15014700), the National Natural Science Foundation of China (No. 61601328), the Scientific Research Plan Project of the Committee of Education in Tianjin (No. JW1708), and the Doctor Foundation of Tianjin Normal University (No. 52XB1417).

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References

  1. Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016)

    Google Scholar 

  2. Rosinol, A., Abate, M., Chang, Y., Carlone, L.: Kimera: an open-source library for real-time metric-semantic localization and mapping. In: IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May–31 August, pp. 1–8 (2020)

    Google Scholar 

  3. Rosinol, A., et al.: Kimera: from SLAM to spatial perception with 3D dynamic scene graphs. ArXiv Preprint arXiv:2101.06894 (2021)

  4. Talak, R., Hu, S., Peng, L., Carlone, L.: Neural trees for learning on graphs. ArXiv Preprint arxiv:2105.07264v1 (2021)

  5. Davison, A.J.: FutureMapping: the computational structure of spatial AI systems. ArXiv Preprint arXiv:1803.11288 (2018)

  6. Davison, A.J., Ortiz, J.: FutureMapping 2: Gaussian belief propagation for spatial AI. ArXiv Preprint arXiv:1910.14139 (2019)

  7. Wada, K., Sucar, E., James, S., Lenton, D., Davison, A.J.: MoreFusion: multi-object reasoning for 6D pose estimation from volumetric fusion. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June, pp. 1–10 (2020)

    Google Scholar 

  8. Sucar, E., Wada, K., Davison, A., NodeSLAM: neural object descriptors for multi-view shape reconstruction. In: International Conference on 3D Vision (3DV), 25–28 Nov, pp. 1–10 (2020)

    Google Scholar 

  9. Czarnowski, J., Laidlow, T., Clark, R., Davison, A.J.: DeepFactors: real-time probabilistic dense monocular SLAM. IEEE Robot. Autom. Lett. 5(2), 721–728 (2020)

    Google Scholar 

  10. Ortiz, J., Pupilli, M., Leutenegger, S., Davison, A.J.: Bundle adjustment on a graph processor. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June, pp. 1–10 (2020)

    Google Scholar 

  11. Ortiz, J., Evans, T., Davison, A.J.: A visual introduction to Gaussian belief propagation. ArXiv Preprint arXiv:2107.02308 (2021)

  12. Ma, Y., Soatto, S., Kosěcká, J., Sastry, S.S.: An Invitation to 3-D Vision: From Images to Geometric Models. Springer, New York (2004). https://doi.org/10.1007/978-0-387-21779-6

    Book  MATH  Google Scholar 

  13. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  14. Wu, Y., Tang, F., Li, H.: Image-based camera localization: an overview. Visual Comput. Ind. Biomed. Art 2018(8), 1–13 (2018)

    Google Scholar 

  15. Long, X.X., et al.: Recent progress in 3D vision. J. Image Graph. 26(6), 1389–1428 (2021)

    Google Scholar 

  16. Vidal, A.R., Rebecq, H., Horstschaefer, T., Scaramuzza, D.: Ultimate SLAM? Combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios. IEEE Robot. Autom. Lett. 3(2), 994–1001 (2018)

    Google Scholar 

  17. Microsoft HoloLens 2. http://www.microsoft.com/en-us/hololens

  18. Huang, G.: Visual-inertial navigation: a concise review. In: IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20–24 May, pp. 1–11 (2019)

    Google Scholar 

  19. Scaramuzza, D., Fraundorfer, F.: Visual odometry: part I: the first 30 years and fundamentals. IEEE Robot. Autom. Mag. 18(4), 80–92 (2011)

    Google Scholar 

  20. Fraundorfer, F., Scaramuzza, D.: Visual odometry: part II: matching, robustness, optimization, and applications. IEEE Robot. Autom. Mag. 19(2), 78–90 (2012)

    Google Scholar 

  21. Agarval, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building Rome in a Day. In: IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, 29 Sept.–2 Oct., pp. 1–8 (2009)

    Google Scholar 

  22. Ozyesil, O., Voroninski, V., Basri, R., Singer, A.: A survey of structure from motion. Acta Numer. 26, 305–364 (2017)

    MathSciNet  MATH  Google Scholar 

  23. Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)

    Google Scholar 

  24. Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017)

    Google Scholar 

  25. Campos, C., Elvira, R., Rodríguez, J.J.G., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Trans. Robot. (Early Access)

    Google Scholar 

  26. Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: fast semi-direct monocular visual odometry. In: IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May–7 June, pp. 1–8 (2014)

    Google Scholar 

  27. Forster, C., Zhang, Z., Gassner, M., Werlberger, M., Scaramuzza, D.: SVO: semidirect visual odometry for monocular and multicamera systems. IEEE Trans. Robot. 33(2), 249–265 (2017)

    Google Scholar 

  28. Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2018)

    Google Scholar 

  29. Kerl, C., Sturm, J., Cremers, D.: Dense visual SLAM for RGB-D cameras. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, 3–7 November, pp. 1–7 (2013)

    Google Scholar 

  30. Zeller, N., Quint, F., Stilla, U.: From the calibration of a light-field camera to direct plenoptic odometry. IEEE J. Sel. Top. Signal Process. 11(7), 1004–1019 (2017)

    Google Scholar 

  31. Saputra, M.R.U., et al.: DeepTIO: a deep thermal-inertial odometry with visual hallucination. IEEE Robot. Autom. Lett. 5(2), 1672–1679 (2020)

    Google Scholar 

  32. Gallego, G., et al.: Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (Early Access)

    Google Scholar 

  33. Kim, H., Leutenegger, S., Davison, A.J.: Real-time 3D reconstruction and 6-DoF tracking with an event camera. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 349–364. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_21

    Chapter  Google Scholar 

  34. Wick, C.: Deep learning. Informatik-Spektrum 40(1), 103–107 (2016). https://doi.org/10.1007/s00287-016-1013-2

    Article  Google Scholar 

  35. Bengio, Y., LeCun, Y., Hinton, G.: Deep learning for AI. Commun. ACM 64(7), 58–65 (2021)

    Google Scholar 

  36. Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DOF camera relocalization. In: IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7–13 December, pp. 1–9 (2015)

    Google Scholar 

  37. Wang, S., Clark, R., Wen, H., Trigoni, N.: DeepVO: towards end-to-end visual odometry with deep recurrent convolutional neural networks. In: IEEE International Conference on Robotics and Automation (ICRA), Singapore, 29 May–3 June, pp. 1–8 (2017)

    Google Scholar 

  38. Yang, N., Stumberg, L.V., Wang, R., Cremers, D.: D3VO: deep depth, deep pose and deep uncertainty for monocular visual odometry. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June, pp. 1–12 (2020)

    Google Scholar 

  39. Chen, C., Wang, B., Lu, C.X., Trigoni, N., Markham, A.: A survey on deep learning for localization and mapping: towards the age of spatial machine intelligence. ArXiv Preprint arXiv:2006.12567v2 (2020)

  40. Wang, Y., Fu, Y., Zheng, R., Wang, L., Qi, J.: New trend in front-end techniques of visual SLAM: from hand-engineered features to deep-learned features. In: International Conference on Artificial Intelligence in China (CHINAAI), 21–22 August, pp. 1–10 (2021)

    Google Scholar 

  41. Saxena, A.: Simultaneous localization and mapping through the lens of nonlinear optimization. Master thesis, University of California, Berkeley, USA (2021)

    Google Scholar 

  42. Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)

    Google Scholar 

  43. Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping: part II. IEEE Robot. Autom. Mag. 13(3), 108–117 (2006)

    Google Scholar 

  44. Anderson, A.J.: Real-time simultaneous localization and mapping with a single camera. In: IEEE International Conference on Computer Vision (ICCV), Nice, France, 13–16 October, pp. 1–8 (2003)

    Google Scholar 

  45. Solá, J., Monin, A., Devy, M., Lemaire, T.: Underlayed initialization in bearing only SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Edmonton, AB, Canada, 2–6 August, pp. 1–6 (2005)

    Google Scholar 

  46. Solá, J.: Simulataneous localization and mapping with the extended Kalman filter: a very quick guide with Matlab code. https://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM

  47. Tully, S., Moon, H., Kantor, G., Choset, H.: Iterated filters for bearing-only SLAM. In: IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, USA, 18–23 May, pp. 1–7 (2008)

    Google Scholar 

  48. Mourikis, A.I., Roumeliotis, S.I.: A multi-state constraint Kalman filter for vision-aided inertial navigation. In: IEEE International Conference on Robotics and Automation (ICRA), Roma, Italy, 10–14 April, pp. 1–8 (2007)

    Google Scholar 

  49. Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, Nara, Japan, 13–16 November, pp. 1–10 (2007)

    Google Scholar 

  50. Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, 6–13 November, pp. 1–8 (2011)

    Google Scholar 

  51. Strasdat, H., Montiel, J.M.M., Davison, A.J.: Real-time monocular SLAM: why filter? In: IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA, 3–7 May, pp. 1–8 (2010). Best Vision Paper Award

    Google Scholar 

  52. Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer, New York (2006). https://doi.org/10.1007/978-0-387-40065-5

    Book  MATH  Google Scholar 

  53. Absil, P.-A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2007)

    MATH  Google Scholar 

  54. Barfoot, T.D.: State Estimation for Robotics. Cambridge University Press, Cambridge (2017)

    Google Scholar 

  55. Solá, J., Deray, J., Atchuthan, D.: A micro lie theory for state estimation in robotics. ArXiv Preprint arXiv:1812.01537 (2018)

  56. Kschischang, F.R., Frey, B.J., Loeliger, H.-A.: Factor graphs and the sum-product algorithm. IEEE Trans. Inform. Theory 47(2), 498–519 (2001)

    MathSciNet  MATH  Google Scholar 

  57. Loeliger, H.-A.: An introduction to factor graphs. IEEE Signal Process. Mag. 21(1), 28–41 (2004)

    Google Scholar 

  58. Dellaert, F., Kaess, M.: Square root SAM: simultaneous localization and mapping via square root information smoothing. Int. J. Robot. Res. 25(12), 1181–1203 (2006)

    MATH  Google Scholar 

  59. Kaess, M., Ranganathan, A., Dellaert, F.: iSAM: incremental smoothing and mapping. IEEE Trans. Robot. 24(6), 1365–1378 (2008)

    Google Scholar 

  60. Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: incremental smoothing and mapping using the Bayes tree. Int. J. Robot. Res. 31(2), 216–235 (2012)

    Google Scholar 

  61. GTSAM (Georgia Tech Smoothing and Mapping). https://gtsam.org

  62. Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g\(^2\)o: a general framework for graph optimization. In: IEEE International Conference on Robotics and Automation (ICRA), 9–13 May, Shanghai, China, pp. 1–7 (2011)

    Google Scholar 

  63. Seres Solver, Google Inc. https://ceres-solver.org

  64. Blanco-Claraco, J.L.: A modular optimization framework for localization and mapping. In: The Robotics: Science and Systems (RSS), Freiburg, Germany, 22–26 June, pp. 1–10 (2019)

    Google Scholar 

  65. Grisetti, G., Kümmerle, R., Stachniss, C., Burgard, W.: A tutorial on graph-based SLAM. IEEE Intell. Transp. Syst. Mag. 2(4), 31–43 (2010)

    Google Scholar 

  66. Dellaert, F.: Factor graphs and GTSAM: a hands-on introduction. Technical report number GT-RIM-CP&R-2012-002 (2012)

    Google Scholar 

  67. Dellaert, F., Kaess, M.: Factor graphs for robot perception. Found. Trends Robot. 6(1–2), 1–139 (2017)

    Google Scholar 

  68. Dellaert, F.: Factor graphs: exploiting structure in robotics. Annu. Rev. Control Robot. Auton. Syst. 4, 141–166 (2021)

    Google Scholar 

  69. Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: On-manifold preintegration for real-time visual-inertial odometry. IEEE Trans. Robot. 33(1), 1–21 (2017)

    Google Scholar 

  70. Sibley, G., Matthies, L., Sukhatme, G.: Sliding window filter with application to planetary landing. J. Field Robot. 27(5), 587–608 (2010)

    Google Scholar 

  71. Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual-inertial odometry using nonlinear optimization. Int. J. Robot. Res. 34(3), 314–334 (2015)

    Google Scholar 

  72. 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)

    Google Scholar 

  73. Chiu, C.-Y.: Simultaneous localization and mapping: a rapprochement of filtering and optimization-based approaches. Master thesis, University of California, Berkeley, USA (2021)

    Google Scholar 

  74. Carlone, L., Tron, R., Daniilidis, K., Dellaert, F.: Initialization techniques for 3D SLAM: a survey on rotation estimation and its use in pose graph optimization. In: IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, 26–30 May, pp. 1–8 (2015)

    Google Scholar 

  75. Rosen, D.M., DuHadway, C., Leonard, J.J.: A convex relaxation for approximate global optimization in simultaneous localization and mapping. In: IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, 26–30 May, pp. 1–8 (2015)

    Google Scholar 

  76. Arrigoni, F., Rossi, B., Fusiello, A.: Spectral synchronization of multiple views in SE(3). SIAM J. Imaging. Sci. 9(4), 1963–1990 (2016)

    MathSciNet  MATH  Google Scholar 

  77. Zhang, Z.: Active robot vision: from state estimation to motion planning. Ph.D. thesis, University of Zurich, Switzerland (2020)

    Google Scholar 

  78. Huang, S., Lai, Y., Frese, U., Dissanayake, G.: How far is SLAM from a linear least squares problem? In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, 18–22 October, pp. 1–6 (2010)

    Google Scholar 

  79. Huang, S., Wang, H., Frese, U., Dissanayake, G.: On the number of local minima to the point feature based SLAM problem. In: IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, USA, 14–18 May, pp. 1–6 (2012)

    Google Scholar 

  80. Wang, H., Hu, G., Huang, S., Dissanayake, G.: On the structure of nonlinearities in pose graph SLAM. In: The Robotics: Science and Systems (RSS), Sydney, Australia, 9–13 July, pp. 1–8 (2012)

    Google Scholar 

  81. Khosoussi, K., Huang, S., Dissanayake, G.: Novel insights into the impact of graph structure on SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, USA, 14–18 September, pp. 1–8 (2014)

    Google Scholar 

  82. Carlone L.: A convergence analysis for pose graph optimization via Gauss-Newton methods. In: IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 6–10 May, pp. 1–8 (2013)

    Google Scholar 

  83. Huang, S., Dissanayake, G.: A critique of current developments in simultaneous localization and mapping. Int. J. Adv. Rob. Syst. 13(5), 1–13 (2016)

    Google Scholar 

  84. Carlone, L., Dellaert, F.: Duality-based verification techniques for 2D SLAM. In: IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 26–30 May, pp. 1–8 (2015)

    Google Scholar 

  85. Carlone, L., Rosen, D.M., Calafiore, G., Leonard, J.J., Dellaert, F.: Lagrangian duality in 3D SLAM: verification techniques and optimal solutions. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28 Sept.–2 October, pp. 1–8 (2015)

    Google Scholar 

  86. Tron, R., Rosen, D.M., Carlone, L.: On the inclusion of determinant constraints in Lagrangian duality for 3D SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Workshop “The Problem of Mobile Sensors: Setting Future Goals and Indicators of Progress for SLAM", Hamburg, Germany, 28 September–2 October, pp. 1–6 (2015)

    Google Scholar 

  87. Carlone, L., Calafiore, G.C., Tommolillo, C., Dellaert, F.: Planar pose graph optimization: duality, optimal solutions, and verification. IEEE Trans. Robot. 32(3), 545–565 (2016)

    Google Scholar 

  88. Rosen, D.M., Carlone, L., Bandeira, A.S., Leonard, J.J.: A certifiably correct algorithm for synchronization over the special Euclidean group. In: Algorithmic Foundations of Robotics XII. SPAR, vol. 13, pp. 64–79. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43089-4_5

    Chapter  Google Scholar 

  89. Rosen, D.M., Carlone, L., Bandeira, A.S., Leonard, J.J.: SE-Sync: a certifiably correct algorithm for synchronization over the special Euclidean group. Computer Science and Artificial Intelligence Laboratory Technical Report MIT-CSAIL-TR-2017-002 (2017)

    Google Scholar 

  90. Rosen, D.M., Carlone, L.: Computational enhancements for certifiably correct SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Workshop “Introspective Methods for Reliable Autonomy”, Vancouver, Canada, 24–28 September, pp. 1–8 (2017)

    Google Scholar 

  91. Rosen, D.M., Carlone, L., Bandeira, A.S., Leonard, J.J.: SE-Sync: a certifiably correct algorithm for synchronization over the special Euclidean group. Intl. J. Robot. Res. 38(2–3), 95–125 (2019)

    Google Scholar 

  92. Latif, Y., Cadena, C., Neira, J.: Robust loop closing over time for pose graph SLAM. Int. J. Robot. Res. 32(14), 1611–1626 (2013)

    Google Scholar 

  93. Latif, Y., Cadena, C., Neira, J.: Robust graph SLAM back-ends: a comparative analysis. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA, 14–18 September, pp. 2683–2690 (2014)

    Google Scholar 

  94. Mangelson, J.G., Dominic, D., Eustice, R.M., Vasudevan, R.: Pairwise consistent measurement set maximization for robust multi-robot map merging. In: IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 21–25 May, pp. 1–8 (2018)

    Google Scholar 

  95. Shi, J., Yang, H., Carlone, L.: ROBIN: a graph-theoretic approach to reject outliers in robust estimation using invariants. In: IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 30 May–5 June, pp. 1–16 (2021)

    Google Scholar 

  96. Huber, P.J., Ronchetti, E.M.: Robust Statistics, 2nd edn. Wiley, New York (2009)

    MATH  Google Scholar 

  97. Sunderhauf, N., Protzel, P.: Switchable constraints for robust pose graph SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7–12 October, Vilamoura, Portugal, pp. 1–6 (2012)

    Google Scholar 

  98. Agarwal, P., Tipaldi, G.D., Spinello, L., Stachniss, C., Burgard, W.: Robust map optimization using dynamic covariance scaling. In: IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 6–10 May, pp. 1–8 (2013)

    Google Scholar 

  99. Olson, E., Agarval, P.: Inference on networks of mixtures for robust robot mapping. Int. J. Robot. Res. 32(7), 826–840 (2013)

    Google Scholar 

  100. Wang, Y.: A review of robust cost functions for M-estimation. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds.) CSPS 2020. LNEE, vol. 654, pp. 743–750. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-8411-4_99

    Chapter  Google Scholar 

  101. Yang, H., Antonante, P., Tzoumas, V., Carlone, L.: Graduated non-convexity for robust spatial perception: from non-minimal solvers to global outlier rejection. IEEE Robot. Autom. Lett. 5(2), 1127–1134 (2020). (Best Paper Award in Robotic Vision at ICRA 2020, Best Paper Award Honorable Mention from RAL 2020)

    Google Scholar 

  102. Antonante, P., Tzoumas, V., Yang, H., Carlone, L.: Outlier-robust estimation: hardness, minimally-tuned algorithms, and applications. ArXiv Preprint arXiv:2007.15109 (2020)

  103. Yang, H., Carlone, L.: One ring to rule them all: certifiably robust geometric perception with outliers. In: Conference on Neural Information Processing Systems (NeurIPS), 6–12 December, pp. 1–14 (2020)

    Google Scholar 

  104. Yang, H., Carlone, L.: Certifiable outlier-robust geometric perception: exact semidefinite relaxations and scalable global optimization. ArXiv Preprint arXiv:2109.03349 (2021)

  105. Rosen, D.M., Doherty, K.J., Espinoza, A.T., Leonard, J.J.: Advances in inference and representation for simultaneous localization and mapping. Annu. Rev. Control Robot. Auton. Syst. 4, 215–242 (2021)

    Google Scholar 

  106. Eriksson, A., Olsson, C., Kahl, F., Chin, T.-J.: Rotation averaging and strong duality. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 18–23 June, pp. 1–9 (2018)

    Google Scholar 

  107. Bustos, A.P., Chin, T.-J., Eriksson, A., Reid, I.: Visual SLAM: why bundle adjust? In: IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, 20–24 May, pp. 1–7 (2019)

    Google Scholar 

  108. Dellaert, F., Rosen, D.M., Wu, J., Mahony, R., Carlone, L.: Shonan rotation averaging: global optimality by surfing \(SO(p)^n\). ArXiv Preprint arXiv:2008.02737 (2020)

  109. Tian, Y., Khosoussi, K., Rosen, D.M., How, J.P.: Distributed certifiably correct pose-graph optimization. IEEE Trans. Robot. (Early Access)

    Google Scholar 

  110. Tian, Y., Chang, Y., Arias, F.H., Nieto-Granda, C., How, J.P., Carlone, L.: Kimera-multi: robust, distributed, dense metric-semantic SLAM for multi-robot systems. ArXiv Preprint arXiv:2106.14386 (2021)

  111. Lajoie, P.-Y., Ramtoula, B., Wu, F., Beltrame, G.: Towards collaborative simultaneous localization and mapping: a survey of current research landscape. ArXiv Preprint arXiv:2108.08325v1 (2021)

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Authors and Affiliations

  1. Key Laboratory of Electronics and Information Technology for Space Systems, Chinese Academy of Sciences, Beijing, 100190, China

    Yue Wang & Xiaodong Peng

  2. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, 300387, China

    Yue Wang

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  1. Yue Wang
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  2. Xiaodong Peng
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Correspondence to Yue Wang .

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Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Wei Wang

  3. Tianjin Normal University, Tianjin, China

    Jiasong Mu

  4. Dalian University of Technology, Dalian, China

    Xin Liu

  5. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

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Wang, Y., Peng, X. (2022). New Trend in Back-End Techniques of Visual SLAM: From Local Iterative Solvers to Robust Global Optimization. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_39

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  • DOI: https://doi.org/10.1007/978-981-16-9423-3_39

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  • Print ISBN: 978-981-16-9422-6

  • Online ISBN: 978-981-16-9423-3

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