Lecture 11 - Particle Filters
- Overview:
This is the third installment in the Localization lectures. Given inputs of laser scan data, a control input, and a map, we want to output a pose and some “particles”. This lecture is divided into three parts: 1) going over the particle filter localization algorithm, 2) tuning the alrogithm, and 3) additional resources and a hands on tutorial. Expect a dense but extremely informative lecture.
- Topics Covered:
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Slides:
- Links to additional resources:
S. Thrun, W. Burgard. “Probabilistic Robotics.” Chapter 4 and Chapter 8.
S. Thrun. “Artificial Intelligence for Robotics, Lesson 3.” Udacity.
S. Thrun, D. Fox, W. Burgard and F. Dellaert. “Robust Monte Carlo Localization for Mobile Robots.” Artificial Intelligence Journal. 2001.
D. Fox, W. Burgard, and S. Thrun. “Markov localization for mobile robots in dynamic environments,” Journal of Artificial Intelligence Research , vol. 11, pp. 391427, 1999.
D. Fox. “KLD-sampling: Adaptive particle filters,” Advances in Neural Information Processing Systems 14 (NIPS), Cambridge, MA, 2002. MIT Press.
D. Bagnell “Particle Filters: The Good, The Bad, The Ugly”
C. Walsh, S. Karaman. “CDDT: Fast Approximate 2D Ray Casting for Accelerated Localization.” Arxiv, 2017.