Literature

Newly created page to list related references and additional literature pertaining to this package.

Direct References

[1.1] Fourie, D., Leonard, J., Kaess, M.: "A Nonparametric Belief Solution to the Bayes Tree" IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), (2016).

[1.2] Fourie, D.: "Multi-modal and Inertial Sensor Solutions for Navigation-type Factor Graphs", Ph.D. Thesis, Massachusetts Institute of Technology Electrical Engineering and Computer Science together with Woods Hole Oceanographic Institution Department for Applied Ocean Science and Engineering, September 2017.

[1.3] Fourie, D., Claassens, S., Pillai, S., Mata, R., Leonard, J.: "SLAMinDB: Centralized graph databases for mobile robotics", IEEE Intl. Conf. on Robotics and Automation (ICRA), Singapore, 2017.

[1.4] Cheung, M., Fourie, D., Rypkema, N., Vaz Teixeira, P., Schmidt, H., and Leonard, J.: "Non-Gaussian SLAM utilizing Synthetic Aperture Sonar", Intl. Conf. On Robotics and Automation (ICRA), IEEE, Montreal, 2019.

[1.5] Doherty, K., Fourie, D., Leonard, J.: "Multimodal Semantic SLAM with Probabilistic Data Association", Intl. Conf. On Robotics and Automation (ICRA), IEEE, Montreal, 2019.

[1.6] Fourie, D., Vaz Teixeira, P., Leonard, J.: "Non-parametric Mixed-Manifold Products using Multiscale Kernel Densities", IEEE Intl. Conf. on Intelligent Robots and Systems (IROS), (2019),.

[1.7] Teixeira, P.N.V., Fourie, D., Kaess, M. and Leonard, J.J., 2019, September. "Dense, sonar-based reconstruction of underwater scenes". In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 8060-8066). IEEE.

[1.8] Fourie, D., Leonard, J.: "Inertial Odometry with Retroactive Sensor Calibration", 2015-2019.

[1.9] Koolen, T. and Deits, R., 2019. Julia for robotics: Simulation and real-time control in a high-level programming language. IEEE, Intl. Conference on Robotics and Automation, ICRA (2019).

[1.10] Fourie, D., Espinoza, A. T., Kaess, M., and Leonard, J. J., “Characterizing marginalization and incremental operations on the Bayes tree,” in International Workshop on Algorithmic Foundations of Robotics (WAFR), 2020, Oulu, Finland, Springer Publishing.

[1.11] Fourie, D., Rypkema, N., Claassens, S., Vaz Teixeira, P., Fischell, E., and Leonard, J.J., "Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation", in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2020, Las Vegas, USA.

[1.12] J. Terblanche, S. Claassens and D. Fourie, "Multimodal Navigation-Affordance Matching for SLAM," in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7728-7735, Oct. 2021, doi: 10.1109/LRA.2021.3098788. Also presented at, IEEE 17th International Conference on Automation Science and Engineering, August 2021, Lyon, France.

Important References

[2.1] Kaess, Michael, et al. "iSAM2: Incremental smoothing and mapping using the Bayes tree" The International Journal of Robotics Research (2011): 0278364911430419.

[2.2] Kaess, Michael, et al. "The Bayes tree: An algorithmic foundation for probabilistic robot mapping." Algorithmic Foundations of Robotics IX. Springer, Berlin, Heidelberg, 2010. 157-173.

[2.3] Kschischang, Frank R., Brendan J. Frey, and Hans-Andrea Loeliger. "Factor graphs and the sum-product algorithm." IEEE Transactions on information theory 47.2 (2001): 498-519.

[2.4] Dellaert, Frank, and Michael Kaess. "Factor graphs for robot perception." Foundations and Trends® in Robotics 6.1-2 (2017): 1-139.

[2.5] Sudderth, E.B., Ihler, A.T., Isard, M., Freeman, W.T. and Willsky, A.S., 2010. "Nonparametric belief propagation." Communications of the ACM, 53(10), pp.95-103

[2.6] Paskin, Mark A. "Thin junction tree filters for simultaneous localization and mapping." in Int. Joint Conf. on Artificial Intelligence. 2003.

[2.7] Farrell, J., and Matthew B.: "The global positioning system and inertial navigation." Vol. 61. New York: Mcgraw-hill, 1999.

[2.8] Zarchan, Paul, and Howard Musoff, eds. Fundamentals of Kalman filtering: a practical approach. American Institute of Aeronautics and Astronautics, Inc., 2013.

[2.9] Rypkema, N. R.,: "Underwater & Out of Sight: Towards Ubiquity in UnderwaterRobotics", Ph.D. Thesis, Massachusetts Institute of Technology Electrical Engineering and Computer Science together with Woods Hole Oceanographic Institution Department for Applied Ocean Science and Engineering, September 2019.

[2.10] Vaz Teixeira, P.: "Dense, Sonar-based Reconstruction of Underwater Scenes", Ph.D. Thesis, Massachusetts Institute of Technology Electrical Engineering and Computer Science together with Woods Hole Oceanographic Institution Department for Applied Ocean Science and Engineering, September 2019.

[2.11] Hanebeck, Uwe D. "FLUX: Progressive State Estimation Based on Zakai-type Distributed Ordinary Differential Equations." arXiv preprint arXiv:1808.02825 (2018).

[2.12] Muandet, Krikamol, et al. "Kernel mean embedding of distributions: A review and beyond." Foundations and Trends® in Machine Learning 10.1-2 (2017): 1-141.

[2.13] Hsiao, M. and Kaess, M., 2019, May. "MH-iSAM2: Multi-hypothesis iSAM using Bayes Tree and Hypo-tree". In 2019 International Conference on Robotics and Automation (ICRA) (pp. 1274-1280). IEEE.

[2.14] Arnborg, S., Corneil, D.G. and Proskurowski, A., 1987. "Complexity of finding embeddings in a k-tree". SIAM Journal on Algebraic Discrete Methods, 8(2), pp.277-284.

[2.15] Sola, J., Deray, J. and Atchuthan, D., 2018. "A micro Lie theory for state estimation in robotics". arXiv preprint arXiv:1812.01537, and tech report.

Additional References

[3.1] Duits, Remco, Erik J. Bekkers, and Alexey Mashtakov. "Fourier Transform on the Homogeneous Space of 3D Positions and Orientations for Exact Solutions to Linear Parabolic and (Hypo-) Elliptic PDEs". arXiv preprint arXiv:1811.00363 (2018).

[3.2] Mohamed, S., Rosca, M., Figurnov, M. and Mnih, A., 2019. "Monte carlo gradient estimation in machine learning". arXiv preprint arXiv:1906.10652.

[3.3] Rackauckas, C., Ma, Y., Martensen, J., Warner, C., Zubov, K., Supekar, R., Skinner, D., Ramadhan, A., Edelman, A., "Universal Differential Equations for Scientific Machine Learning", Archive online, DOI: 2001.04385.

[3.4] Boumal, Nicolas. An introduction to optimization on smooth manifolds. Available online, May, 2020.

[3.5] Relationship between the Hessianand Covariance Matrix forGaussian Random Variables, John Wiley & Sons

[3.6]. Pennec, Xavier. Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements, HAL Archive, 2011, Inria, France.

Signal Processing (Beamforming and Channel Deconvolution)

[4.1] Van Trees, H.L., 2004. Optimum array processing: Part IV of detection, estimation, and modulation theory. John Wiley & Sons.

[4.2a] Dowling, D.R., 2013. "Acoustic Blind Deconvolution and Unconventional Nonlinear Beamforming in Shallow Ocean Environments". MICHIGAN UNIV ANN ARBOR DEPT OF MECHANICAL ENGINEERING.

[4.2b] Hossein Abadi, S., 2013. "Blind deconvolution in multipath environments and extensions to remote source localization", paper, thesis.