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), under-review.

[1.7] Fourie, D., Leonard, J.: "Inertial Odometry with Retroactive Sensor Calibration", publication under review.

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

[2.10] 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.

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).