Bibliography

8. Bibliography#

[ACC+22]

Michal Adamkiewicz, Timothy Chen, Adam Caccavale, Rachel Gardner, Preston Culbertson, Jeannette Bohg, and Mac Schwager. Vision-only robot navigation in a neural radiance world. IEEE Robotics and Automation Letters, 7(2):4606–4613, 2022. doi:10.1109/LRA.2022.3150497.

[BP66]

Leonard E. Baum and Ted Petrie. Statistical Inference for Probabilistic Functions of Finite State Markov Chains. The Annals of Mathematical Statistics, 37(6):1554 – 1563, 1966. doi:10.1214/aoms/1177699147.

[BM12]

Randal W. Beard and Timothy W. McLain. Small Unmanned Aircraft: Theory and Practice. Princeton University Press, February 2012.

[BK60]

Richard Bellman and Robert Kalaba. Dynamic programming and adaptive processes I: mathematical foundation. IRE Transactions on Automatic Control, AC-5(1):5–10, 1960. doi:10.1109/TAC.1960.6429288.

[BM92]

P.J. Besl and Neil D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239–256, 1992. doi:10.1109/34.121791.

[Cha23]

Stanley H. Chan. Introduction to Probability for Data Science. Michigan Publishing Services, 2023. ISBN 978-1-60785-747-1. URL: https://probability4datascience.com/index.html.

[CLH+05]

Howie Choset, Kevin M. Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia E. Kavraki, and Sebastian Thrun. Principles of Robot Motion. MIT Press, 2005. ISBN 9780262033275. URL: https://mitpress.mit.edu/9780262033275/principles-of-robot-motion/.

[Del21]

Frank Dellaert. Factor graphs: exploiting structure in robotics. Annual Review of Control, Robotics, and Autonomous Systems, 4:141–166, 2021. doi:10.1146/annurev-control-061520-010504.

[DFBT99]

Frank Dellaert, Dieter Fox, Wolfram Burgard, and Sebastian Thrun. Monte carlo localization for mobile robots. In Proceedings 1999 IEEE International Conference on Robotics and Automation, volume 2, 1322–1328 vol.2. 1999. doi:10.1109/ROBOT.1999.772544.

[DK17]

Frank Dellaert and Michael Kaess. Factor graphs for robot perception. Foundations and Trends® in Robotics, 6(1-2):1–139, 2017. URL: https://www.nowpublishers.com/article/Details/ROB-043, doi:10.1561/2300000043.

[DHS12]

R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. Wiley, 2012. ISBN 9781118586006. URL: https://books.google.co.uk/books?id=Br33IRC3PkQC.

[Far08]

Jay A. Farrell. Aided Navigation: GPS with High Rate Sensors. McGraw-Hill, 2008.

[FCDS16]

C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza. On-manifold preintegration for real-time visual-inertial odometry. IEEE Transactions on Robotics, 2016. doi:10.1109/TRO.2016.2597321.

[FranccoisLHI+18]

Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, and Joelle Pineau. An introduction to deep reinforcement learning. Foundations and Trends in Machine Learning, 11(3–4):219–354, 2018. URL: http://dx.doi.org/10.1561/2200000071, doi:10.1561/2200000071.

[GBKL19]

Kanishke Gamagedara, Mahdis Bisheban, Evan Kaufman, and Taeyoung Lee. Geometric controls of a quadrotor uav with decoupled yaw control. In 2019 American Control Conference (ACC), 3285–3290. 2019. doi:10.23919/ACC.2019.8815189.

[GBC16]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. ISBN 9780262035613. URL: https://www.deeplearningbook.org.

[Hal98]

Anders Hald. A history of mathematical statistics from 1750 to 1930. Wiley, 1998.

[Hal03]

Anders Hald. A History of Probability and Statistics and Their Applications before 1750. Wiley, 2003.

[HZ00]

Richard Hartley and Andrew Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2000.

[IPAC10]

Viorela Ila, Josep M. Porta, and Juan Andrade-Cetto. Information-based compact pose slam. IEEE Transactions on Robotics, 26(1):78–93, 2010. doi:10.1109/TRO.2009.2034435.

[JBM75]

F. Jelinek, L. Bahl, and R. Mercer. Design of a linguistic statistical decoder for the recognition of continuous speech. IEEE Transactions on Information Theory, 21(3):250–256, 1975. doi:10.1109/TIT.1975.1055384.

[KF09]

D. Koller and N. Friedman. Probabilistic Graphical Models Principles and Techniques. The MIT Press, 2009.

[Lat91]

Jean-Claude Latombe. Robot Motion Planning. Kluwer Academic Publishers, USA, 1991. ISBN 0792391292.

[LaV06]

Steven M. LaValle. Planning Algorithms. Cambridge University Press, 2006. ISBN 9780521862059. URL: https://lavalle.pl/planning/.

[LLZ+20]

Zhiyuan Li, Huawei Liang, Pan Zhao, Shaobo Wang, and Hui Zhu. Efficient lane change path planning based on quintic spline for autonomous vehicles. In 2020 IEEE International Conference on Mechatronics and Automation (ICMA), 338–344. 2020. doi:10.1109/ICMA49215.2020.9233841.

[LS12]

Todd Lupton and Salah Sukkarieh. Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions. IEEE Transactions on Robotics, 28(1):61–76, Feb 2012. doi:10.1109/TRO.2011.2170332.

[LP17]

Kevin M. Lynch and Frank C. Park. Modern Robotics: Mechanics, Planning, and Control. Cambridge University Press, USA, 2017. ISBN 1107156300. URL: http://modernrobotics.org/.

[MSKS04]

Yi Ma, Stefano Soatto, Jana Kosecka, and Shankar S. Sastry. An Invitation to 3-D Vision. Springer, 2004.

[MKC12]

Robert Mahony, Vijay Kumar, and Peter Corke. Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. Robotics and Automation Magazine (RAM), 2012. doi:10.1109/MRA.2012.2206474.

[MST+21]

Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: representing scenes as neural radiance fields for view synthesis. Commun. ACM, 65(1):99–106, dec 2021. doi:10.1145/3503250.

[MKS+15]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, February 2015. URL: https://doi.org/10.1038/nature14236, doi:10.1038/nature14236.

[MLS94]

Richard M. Murray, Zexiang Li, and Shankar S. Sastry. A Mathematical Introduction to Robotic Manipulation. CRC Press, 1994.

[Pea88]

J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., 1988.

[Sch66]

Peter Schoenemann. A generalized solution of the orthogonal procrustes problem. Psychometrika, 31(1):1–10, March 1966. doi:10.1007/BF02289451.

[SWD+17]

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. CoRR, 2017. URL: http://arxiv.org/abs/1707.06347, arXiv:1707.06347.

[SNS11]

Roland Siegwart, Illah Reza Nourbakhsh, and Davide Scaramuzza. Introduction to Autonomous Mobile Robots. MIT Press, 2011. ISBN 9780262015356. URL: https://mitpress.mit.edu/9780262015356/introduction-to-autonomous-mobile-robots/.

[SS73]

Richard D. Smallwood and Edward J. Sondik. The optimal control of partially observable Markov processes over a finite horizon. Operations Research, 21(5):1071–1088, Sep. 1973.

[Son78]

Edward J. Sondik. The optimal control of partially observable Markov processes over the infinite horizon: discounted costs. Operations Research, 26(2):282–304, March 1978.

[SHV06]

M. Spong, S. Hutchinson, and M. Vidyasagar. Robot Modeling and Control. John Wiley and Sons, NY, NY, 2006.

[SSC22]

Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: super-fast convergence for radiance fields reconstruction. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5449–5459. 2022. doi:10.1109/CVPR52688.2022.00538.

[SB18]

Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, Cambridge, MA, USA, 2 edition, 2018. ISBN 9780262039246. URL: http://incompleteideas.net/book/the-book-2nd.html.

[TBF05]

Sebastian Thrun, Wolfram Burgard, and Dieter Fox. Probabilistic Robotics. The MIT Press, 2005. ISBN 9780262201629. URL: https://mitpress.mit.edu/9780262201629/probabilistic-robotics/.

[Wat89]

Christopher John Cornish Hellaby Watkins. Learning from delayed rewards. King's College, Cambridge United Kingdom, 1989.

[WZKT10]

Moritz Werling, Julius Ziegler, Sören Kammel, and Sebastian Thrun. Optimal trajectory generation for dynamic street scenarios in a frenét frame. In 2010 IEEE International Conference on Robotics and Automation, 987–993. 2010. doi:10.1109/ROBOT.2010.5509799.

[Wil92]

R. J. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8:229–259, 1992.

[ZLLS20]

Aston Zhang, Zack Lipton, Mu Li, and Alexander J. Smola. Dive into Deep Learning. d2l.ai, 2020. ISBN 978-1009389433. URL: https://d2l.ai/.