A review of optimisation strategies used in simultaneous localisation and mapping

References

• Agarwal P., Tipaldi G. D., Spinello L., Stachniss C., & Burgard M. (2013). Robust map optimization using dynamic covariance scaling. In Proceedings of IEEE international conference on robotics automation (pp. 62–69).
   Google Scholar
• Agarwal S., & Mierle K. (2016). Ceres Solver. Retrieved from http://ceres-solver.org.
   Google Scholar
• Bai F., Huang S., Vidal Calleja T.A., & Zhang Q. (2016). Incremental SQP method for constrained optimization formulation in SLAM. In IEEE international conference on control, automation, robotics and vision, October.
   Google Scholar
• Bai F., Vidal-Calleja T., & Huang S. (2018). Robust incremental SLAM under constrained optimization formulation. IEEE Robotics and Automation Letters, 3(2), 1207–1214. doi: 10.1109/LRA.2018.2794610
   Web of Science ®Google Scholar
• Briales J., & Gonzalez-Jimenez J. (2017). Cartan-sync: Fast and global SE (d)-synchronization. IEEE Robotics and Automation Letters, 2(4), 2127–2134. doi: 10.1109/LRA.2017.2718661
   Web of Science ®Google Scholar
• Cadena C., Carlone L., Carrillo H., Latif Y., Scaramuzza D., Neira J., Reid I., & J. J. Leonard (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332. doi: 10.1109/TRO.2016.2624754
   Web of Science ®Google Scholar
• Carlevaris-Bianco N., & Eustice R. M. (2013). Generic factor-based node marginalization and edge sparsification for pose-graph SLAM. In Proceedings of IEEE international conference on robotics automation (pp. 5748–5755).
   Google Scholar
• Carlone L. (2013). Convergence analysis of pose graph optimization via Gauss–Newton methods. In Proceedings of IEEE international conference on robotics automation (pp. 965–972).
   Google Scholar
• Carlone L., Calafiore G., Tommolillo C., & Dellaert F. (2016). Planar pose graph optimization: Duality, optimal solutions, and verification, IEEE Transactions on Robotics, 32(3), 545–565. doi: 10.1109/TRO.2016.2544304
   Web of Science ®Google Scholar
• Carlone L., & Calafiore G. C. (2018). Convex relaxations for pose graph optimization with outliers. IEEE Robotics and Automation Letters, 3(2), 1160–1167. doi: 10.1109/LRA.2018.2793352
   Web of Science ®Google Scholar
• Carlone L., Censi A., & Dellaert F. (2014). Selecting good measurements via relaxation: A convex approach for robust estimation over graphs. In Proceedings of IEEE/RSJ international conference on intellectual robots system (pp. 2667–2674).
   Google Scholar
• Carlone L., Tron R., Daniilidis K., & Dellaert F. (2015). Initialization techniques for 3D SLAM: A survey on rotation estimation and its use in pose graph optimization, In Proceedings of IEEE international conference on robotics automation (pp. 4597–4604). Seattle, WA, USA.
   Google Scholar
• Dellaert F., & Kaess M. (2006). Square root SAM: Simultaneous localization and mapping via square root information smoothing. International Journal of Robotics Research, 25(12), 1181–1203. doi: 10.1177/0278364906072768
   Web of Science ®Google Scholar
• Dissanayake G., Newman P., Clark S., Durrant-Whyte H., & Csorba M. (2001). A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation, 17(3), 229–241. doi: 10.1109/70.938381
   Google Scholar
• Dubbelman G., & Brownig B. (2015). COP-SLAM: Closed-form online pose-chain optimisation for visual SLAM. IEEE Transactions on Robotics, 31(5), 1194–1213. doi: 10.1109/TRO.2015.2473455
   Web of Science ®Google Scholar
• Furgale P., Tong C. H., Barfoot T. D., & Sibley G. (2015). Continuous-time batch trajectory estimation using temporal basis functions. The International Journal of Robotics Research, 34(14), 1688–1710. doi: 10.1177/0278364915585860
   Web of Science ®Google Scholar
• Graham M. C., How J. P., & Gustafson D. E. (2015). Robust incremental slam with consistency-checking. In Proceedings of IEEE/RSJ international conference on intellectual robots system (pp. 117–124).
   Google Scholar
• Grisetti G., Kummerle R., & Ni K. (2012). Robust optimisation of factor graphs by using condensed measurements, In Proceeding of IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 581–588).
   Google Scholar
• Grisetti G., Kummerle R., Stachniss C., Frese U., & Hertzberg C. (2010). Hierarchical optimisation on manifolds for online 2D and 3D mapping, In Proceeding of IEEE international conference on robotics and automation (ICRA) (pp. 273–278).
   Google Scholar
• Grisetti G., Stachniss C., & Burgard W. (2009). Non-linear constraint network optimisation for efficient map learning. IEEE Transactions on Intelligent Transportation Systems, 10(3), 428–439. doi: 10.1109/TITS.2009.2026444
   Web of Science ®Google Scholar
• Hartley R., Trumpf J., Dai Y., & Li H. (2013). Rotation averaging. International Journal of Computer Vision, 103(3), 267–305. doi: 10.1007/s11263-012-0601-0
   Web of Science ®Google Scholar
• Huang G., Kaess M., & Leonard J. J. (2013). Consistent sparsification for graph optimization. In Proceedings of Europaea conference on mobile robots (pp. 150–157).
   Google Scholar
• Huang S., Lai Y., Frese U., & Dissanayake G. (2010). How far is SLAM from a linear least squares problem? In Proceedings of international conference on intelligent robots and systems (IROS) (pp. 3011–3016).
   Google Scholar
• Huang S., Wang H., Frese H., & Dissanayake G. (2012). On the number of local minima to the point feature based SLAM problem. In Proceedings of IEEE international conference on robotics and automation (ICRA) (pp. 2074–2079).
   Google Scholar
• Huang S., Wang Z., Dissanayake G., & Frese U. (2008). Iterated SLSJF: A sparse local submap joining algorithm with improved consistency. In Proceedings of Australasian conference on robotics and automation.
   Google Scholar
• Huang S., Wang Z., Dissanayake G., & Frese U. (2009). Iterated D-SLAM map joining: Evaluating its performance in terms of consistency, accuracy and efficiency. Autonomous Robots, 27(4), 409–429. doi: 10.1007/s10514-009-9153-8
   Web of Science ®Google Scholar
• Huber P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. doi: 10.1214/aoms/1177703732
   Google Scholar
• Ila V., Polok L., Solony M., & Svoboda P. (2017). SLAM++-A highly efficient and temporally scalable incremental SLAM framework. The International Journal of Robotics Research, 36(2), 210–230. doi: 10.1177/0278364917691110
   Web of Science ®Google Scholar
• Ila V., Porta J. M., & Andrade-Cetto J. (2010). Information-based compact pose SLAM. IEEE Transactions on Robotics, 26(1), 78–93. doi: 10.1109/TRO.2009.2034435
   Web of Science ®Google Scholar
• Kaess M., Johannsson H., Roberts R., Ila V., Leonard J. J., & Dellaert F. (2012). iSAM2: Incremental smoothing and mapping using the Bayes tree. The International Journal of Robotics Research, 31(2), 216–235. doi: 10.1177/0278364911430419
   Web of Science ®Google Scholar
• Kaess M., Ranganathan A., & Dellaert F. (2008). iSAM: Incremental smoothing and mapping. IEEE Transactions on Robotics, 24(6), 1365–1378. doi: 10.1109/TRO.2008.2006706
   Web of Science ®Google Scholar
• Khosoussi K., Huang S., & Dissanayake G. (2016). A sparse separable SLAM back-end. IEEE Transactions on Robotics, 32(6), 1536–1549. doi: 10.1109/TRO.2016.2609394
   Web of Science ®Google Scholar
• Konolige K., & Agrawal M. (2009). FrameSLAM: From bundle adjustment to real-time visual mapping. IEEE Transactions on Robitics, 24(5), 1066–1077. doi: 10.1109/TRO.2008.2004832
   Web of Science ®Google Scholar
• Konolige K., Grisetti G., Kummerle R., Limketkai B., & Vincent R. (2010). Efficient sparse pose adjustment for 2D mapping, In Proceedings of international conference on intelligent robots and systems (IROS) (pp. 22–29).
   Google Scholar
• Kretzschmar H., Stachniss C., & Grisetti G. (2011). Efficient information theoretic graph pruning for graph-based SLAM with laser range finders. In Proceedings of IEEE/RSJ intelligent robots and system (pp. 865–871).
   Google Scholar
• Kummerle R., Grisetti G., Strasdat H., Konolige K., & Burgard W. (2011). g2o: A general framework for graph optimisation, In Proceedings of IEEE international conference on robotics and automation (ICRA) (pp. 3607–3613).
   Google Scholar
• Latif Y., Cadena C., & Neira J. (2013). Robust loop closing over time for pose graph SLAM. The International Journal of Robotics Research, 32(14), 1611–1626. doi: 10.1177/0278364913498910
   Web of Science ®Google Scholar
• Liu M., Huang S., Dissanayake G., & Wang H. (2012). A convex optimization based approach for pose SLAM problems. In Proceedings of IEEE/RSJ intelligent robots and system (pp. 1898–1903).
   Google Scholar
• Lu F., & Milios E. (1997). Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4,333–349. doi: 10.1023/A:1008854305733
   Web of Science ®Google Scholar
• Mazuran M., Burgard W., & Tipaldi G. D. (2006). Nonlinear factor recovery for long-term SLAM. The International Journal of Robotics Research, 35(1–3), 50–72. doi: 10.1177/0278364915581629
   Google Scholar
• Montiel J. M. M., Civera J., & Davison A. J. (2006). Unified inverse depth parametrization for monocular SLAM. In Proceedings of robotic, science system conference (pp. 81–88), August.
   Google Scholar
• Ni K., & Dellaert F. (2010). Multi-level submap based SLAM using nested dissection, In Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2558–2565).
   Google Scholar
• Olson E., & Agarwal P. (2013). Inference on networks of mixtures for robust robot mapping. The International Journal of Robotics Research, 32(7), 826–840. doi: 10.1177/0278364913479413
   Web of Science ®Google Scholar
• Olson E., Leonard J., & Teller S. (2006). Fast iterative optimisation of pose graphs with poor initial estimates. In Proceedings of IEEE international conference on robotics and automation (ICRA) (pp. 2262–2269).
   Google Scholar
• Rosen D. M., Carlone L., Bandeira A. S., & Leonard J. J. (2016). SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group. In Proceedings of 12th international workshop on the algorithmic foundations of robotics (WAFR).
   Google Scholar
• Rosen D. M., DuHadway C., & Leonard J. J. (2015). A convex relaxation for approximate global optimization in simultaneous localization and mapping. In Proceedings of IEEE international conference on robotics automation (pp. 5822–5829). Seattle, WA, USA.
   Google Scholar
• Sola J., Vidal-Calleja T., Civera J., & Montiel J. M. M. (2012). Impact of landmark parametrization on monocular EKF-SLAM with points and lines. International Journal of Computer Vision, 97(3), 339–368. doi: 10.1007/s11263-011-0492-5
   Web of Science ®Google Scholar
• Sunderhauf N., & Protzel P. (2012). Towards a robust back end for pose graph SLAM. In Proceedings of IEEE international conference on robotics automation (pp. 1254–1261).
   Google Scholar
• Wang H., Hu G., Huang S., & Dissanayake G. (2012). On the structure of nonlinearities in pose graph SLAMi In Proceedings of robotics: science and systems (RSS).
   Google Scholar
• Wang H., Huang S., Frese U., & Dissanayake G. (2013). The nonlinearity structure of point feature SLAM problems with spherical covariance matrices. Automatica, 49(10), 3112–3119. doi: 10.1016/j.automatica.2013.07.025
   Web of Science ®Google Scholar
• Wang H., Huang S., Khosoussi K., Frese U., Dissanayake G., & Liu B. (2015). Dimension reduction for point feature SLAM problems with spherical covariance matrices. Automatica, 51, 149–157. doi: 10.1016/j.automatica.2014.10.114
   Web of Science ®Google Scholar
• Wang H., Huang S., Yang G., & Dissanayake G. (2018). Comparison of two different objective functions in 2D point feature SLAM. Automatica, 97, 172–181. doi: 10.1016/j.automatica.2018.08.009
   Web of Science ®Google Scholar
• Zhang J., Kaess M., & Singh S. (2016). On degeneracy of optimization-based state estimation problems. In Proceedings of IEEE international conference on robotics automation (pp. 809–816).
   Google Scholar
• Zhang T., Wu K., Song J., Huang S., & Dissanayake G. (2017). Convergence and consistency analysis for a 3-D invariant-EKF SLAM. IEEE Robotics and Automation Letters, 2(2), 733–740. doi: 10.1109/LRA.2017.2651376
   Web of Science ®Google Scholar
• Zhao L., Huang S., & Dissanayake G. (2013). Linear SLAM: A linear solution to the feature based and pose graph SLAM based on submap joining, In Proceedings of international conference on intelligent robots and systems (IROS) (pp. 24–30).
   Google Scholar
• Zhao L., Huang S., & Dissanayake G. (2014). Linear MonoSLAM: A linear approach to large-scale monocular SLAM problems, In Proceedings of IEEE international conference on robotics and automation (ICRA) (pp. 1517–1523).
   Google Scholar
• Zhao L., Huang S., Sun Y., Yan L., & Dissanayake G. (2015). ParallaxBA: Bundle adjustment using parallax angle feature parametrization. The International Journal of Robotics Research, 34(4–5), 493–516. doi:10.1177/0278364914551583.
   Web of Science ®Google Scholar