Skip to main content

A Reduced Parallel Transport Equation on Lie Groups with a Left-Invariant Metric

  • Conference paper
  • First Online:
Geometric Science of Information (GSI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12829))

Included in the following conference series:

Abstract

This paper presents a derivation of the parallel transport equation expressed in the Lie algebra of a Lie group endowed with a left-invariant metric. The use of this equation is exemplified on the group of rigid body motions SE(3), using basic numerical integration schemes, and compared to the pole ladder algorithm. This results in a stable and efficient implementation of parallel transport. The implementation leverages the python package and is available online.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arnold, V.: Sur la géométrie différentielle des groupes de Lie de dimension infinie et ses applications à l’hydrodynamique des fluides parfaits. Annales de l’institut Fourier 16(1), 319–361 (1966). https://doi.org/10.5802/aif.233

    Article  MathSciNet  MATH  Google Scholar 

  2. Barbaresco, F., Gay-Balmaz, F.: Lie group cohomology and (Multi) symplectic integrators: new geometric tools for lie group machine learning based on souriau geometric statistical mechanics. Entropy 22(5), 498 (2020). https://doi.org/10.3390/e22050498

    Article  MathSciNet  Google Scholar 

  3. Barrau, A., Bonnabel, S.: The invariant extended kalman filter as a stable observer. IEEE Trans. Autom. Control 62(4), 1797–1812 (2017). https://doi.org/10.1109/TAC.2016.2594085

    Article  MathSciNet  MATH  Google Scholar 

  4. Brooks, D., Schwander, O., Barbaresco, F., Schneider, J.Y., Cord, M.: Riemannian batch normalization for SPD neural networks. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 15489–15500. Curran Associates, Inc. (2019)

    Google Scholar 

  5. Cendra, H., Holm, D.D., Marsden, J.E., Ratiu, T.S.: Lagrangian reduction, the euler-Poincaré equations, and semidirect products. Am. Math. Soc. Translations 186(1), 1–25 (1998)

    MATH  Google Scholar 

  6. Gallier, J., Quaintance, J.: Differential Geometry and Lie Groups. GC, vol. 12. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46040-2

    Book  MATH  Google Scholar 

  7. Gay-Balmaz, F., Holm, D.D., Meier, D.M., Ratiu, T.S., Vialard, F.X.: Invariant higher-order variational problems II. J Nonlinear Sci. 22(4), 553–597 (2012). https://doi.org/10.1007/s00332-012-9137-2, http://arxiv.org/abs/1112.6380

  8. Guigui, N., Pennec, X.: Numerical Accuracy of Ladder Schemes for Parallel Transport on Manifolds (2021). https://hal.inria.fr/hal-02894783

  9. Hauberg, S., Lauze, F., Pedersen, K.S.: Unscented Kalman filtering on Riemannian manifolds. J. Math. Imaging Vis. 46(1), 103–120 (2013). https://doi.org/10.1007/s10851-012-0372-9

    Article  MathSciNet  MATH  Google Scholar 

  10. Iserles, A., Munthe-Kaas, H., Nørsett, S., Zanna, A.: Lie-group methods. Acta Numerica (2005). https://doi.org/10.1017/S0962492900002154

  11. Journée, M., Absil, P.-A., Sepulchre, R.: Optimization on the orthogonal group for independent component analysis. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 57–64. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74494-8_8

    Chapter  MATH  Google Scholar 

  12. Kim, K.R., Dryden, I.L., Le, H., Severn, K.E.: Smoothing splines on Riemannian manifolds, with applications to 3D shape space. J. Royal Stat. Soc. Series B (Statistical Methodology) 83(1), 108–132 (2020)

    Google Scholar 

  13. Kolev, B.: Lie Groups and mechanics: an introduction. J. Nonlinear Math. Phys. 11(4), 480–498 (2004). https://doi.org/10.2991/jnmp.2004.11.4.5. arXiv: math-ph/0402052

  14. Lorenzi, M., Pennec, X.: Efficient parallel transport of deformations in time series of images: from schild to pole ladder. J. Math. Imaging Vis. 50(1), 5–17 (2014). https://doi.org/10.1007/s10851-013-0470-3

    Article  MATH  Google Scholar 

  15. Mahony, R., Manton, J.H.: The geometry of the Newton method on non-compact lie groups. J. Global Optim. 23(3), 309–327 (2002). https://doi.org/10.1023/A:1016586831090

    Article  MathSciNet  MATH  Google Scholar 

  16. Marsden, J.E., Ratiu, T.S.: Mechanical systems: symmetries and reduction. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 5482–5510. Springer, New York (2009). https://doi.org/10.1007/978-0-387-30440-3_326

  17. Milnor, J.: Curvatures of left invariant metrics on lie groups. Adv. Math. 21(3), 293–329 (1976). https://doi.org/10.1016/S0001-8708(76)80002-3

    Article  MathSciNet  MATH  Google Scholar 

  18. Miolane, N., et al.: Geomstats: a python package for riemannian geometry in machine learning. J. Mach. Learn. Res. 21(223), 1–9 (2020). http://jmlr.org/papers/v21/19-027.html

  19. Nava-Yazdani, E., Hege, H.C., Sullivan, T.J., von Tycowicz, C.: Geodesic analysis in Kendall’s shape space with epidemiological applications. J. Math. Imaging Vis. 62(4), 549–559 (2020). https://doi.org/10.1007/s10851-020-00945-w

    Article  MathSciNet  MATH  Google Scholar 

  20. Pennec X., Arsigny, V.: Exponential barycenters of the canonical cartan connection and invariant means on lie groups. In: Nielsen, F., Bhatia, R. (eds.) Matrix Information Geometry, pp. 123–166. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30232-9_7

  21. Yair, O., Ben-Chen, M., Talmon, R.: Parallel transport on the cone manifold of SPD matrices for domain adaptation. IEEE Trans. Sig. Process. 67, 1797–1811 (2019). https://doi.org/10.1109/TSP.2019.2894801

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work was partially funded by the ERC grant Nr. 786854 G-Statistics from the European Research Council under the European Union’s Horizon 2020 research and innovation program. It was also supported by the French government through the 3IA Côte d’Azur Investments ANR-19-P3IA-0002 managed by the National Research Agency.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicolas Guigui .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guigui, N., Pennec, X. (2021). A Reduced Parallel Transport Equation on Lie Groups with a Left-Invariant Metric. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2021. Lecture Notes in Computer Science(), vol 12829. Springer, Cham. https://doi.org/10.1007/978-3-030-80209-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-80209-7_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80208-0

  • Online ISBN: 978-3-030-80209-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics