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These books and papers go deeper into the topics of this book. They are cited on the pages where they are most relevant, and the full reference list appears at the bottom of each page.

General robotics. Siegwart, Nourbakhsh, and Scaramuzza give a broad introduction to mobile robots Siegwart et al., 2011. Lynch and Park cover kinematics, dynamics, and control of robot arms and mobile robots rigorously Lynch & Park, 2017. Corke pairs every topic with code Corke, 2017. Dellaert and Hutchinson’s online textbook develops probabilistic robotics with factor graphs and the GTSAM library Dellaert & Hutchinson, 2024.

Probabilistic robotics. Thrun, Burgard, and Fox is the standard reference for Bayes filters, particle filters, occupancy grids, and SLAM Thrun et al., 2005. Rabiner’s tutorial is a clear introduction to hidden Markov models Rabiner, 1989. Kalman’s original paper introduced the Kalman filter Kalman, 1960.

Decisions and planning. Russell and Norvig cover decision theory and search Russell & Norvig, 2020. Bellman introduced dynamic programming Bellman, 1957. LaValle’s Planning Algorithms is the comprehensive reference for motion planning LaValle, 2006, and A* was introduced by Hart, Nilsson, and Raphael Hart et al., 1968.

Perception. Szeliski’s book covers computer vision from image formation to deep learning Szeliski, 2022. Elfes introduced occupancy grids Elfes, 1989.

Vehicles and aerial robots. Coulter describes the pure pursuit path tracker Coulter, 1992. Mellinger and Kumar develop smooth trajectories for quadrotors Mellinger & Kumar, 2011.

References
  1. Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to Autonomous Mobile Robots (2nd ed.). MIT Press.
  2. Lynch, K. M., & Park, F. C. (2017). Modern Robotics: Mechanics, Planning, and Control. Cambridge University Press.
  3. Corke, P. (2017). Robotics, Vision and Control: Fundamental Algorithms in MATLAB (2nd ed.). Springer.
  4. Dellaert, F., & Hutchinson, S. (2024). Introduction to Robotics and Perception. Online textbook (draft), Georgia Institute of Technology. https://www.roboticsbook.org
  5. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. MIT Press.
  6. Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2), 257–286. 10.1109/5.18626
  7. Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35–45. 10.1115/1.3662552
  8. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
  9. Bellman, R. (1957). Dynamic Programming. Princeton University Press.
  10. LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press.
  11. Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107. 10.1109/TSSC.1968.300136
  12. Szeliski, R. (2022). Computer Vision: Algorithms and Applications (2nd ed.). Springer.
  13. Elfes, A. (1989). Using Occupancy Grids for Mobile Robot Perception and Navigation. Computer, 22(6), 46–57. 10.1109/2.30720
  14. Coulter, R. C. (1992). Implementation of the Pure Pursuit Path Tracking Algorithm (Techreport CMU-RI-TR-92-01). Carnegie Mellon University, Robotics Institute.
  15. Mellinger, D., & Kumar, V. (2011). Minimum Snap Trajectory Generation and Control for Quadrotors. IEEE International Conference on Robotics and Automation (ICRA), 2520–2525. 10.1109/ICRA.2011.5980409
Appendices
Notation