Probabilistic robotics
by Sebastian Thrun, Dieter Fox, Wolfram Burgard · first published 2005
76% recommend24% neutral
Why HN recommends it
Explains Bayes filtering and the Kalman filter family (EKF, UKF, particle filters) clearly, with coverage of SLAM, perception and sensor fusion. Suits readers who want a formal, ML-linked treatment of robotics.
Summarised by Claude from recommending comments
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In Robotics / Computer Vision: - Thrun et. al, Probabilistic Robotics - Multiple View Geometry, Hartley / Zisserman - An Invitation to 3D Vision, Ma - Pattern Recognition and Machine Learning, Bishop -Convex Optimization, Boyd
on “Ask HN: What are the best textbooks in your field of expertise?” ↗…(warning TOUGH!!) Robotics. (w) Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning) - not about robotics per se but useful to understand the next book (x) Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) by Thrun, Burgard and Fox PS: I own all these books (except AIMA 3 for which I only have pre pub pdfs) and if any HN folks in Bangalore want to browse before…
on “Learning About Statistical Learning” ↗…it sounds. Brings new meaning to the phrase "corner cases". Books: An oldie but goodie that gives serious beginners a good overview is "Behavior Based Robotics" by Ron Arkin. A little more intense: "Probabilistic Robotics", Thrun/Burgard/Fox. And my favorite piece of advice to all robot beginners: Your first robot should have low enough mass that a missing semicolon does not put a hole the the wallboard of your living…
on “Robot Academy – Open online robotics education resource” ↗…approach". http://www.amazon.com/Computer-Vision-Approach-David-Forsyth... I would also recommend Mitchell's "Machine Learning", Strang's "Linear Algebra and it's applications", and throw in Thrun's new "Probabilistic Robotics" for fun.
on “ComputerVisionWiki.org” ↗…cited papers for a topic and then find their lectures on videolectures.net Other's already mentioned Jordan, Bishop and Friedman - these are all great I really liked Thrun, Burgard, and Fox's text Probabilistic Robotics - they use a lot of ML like algorithms under very tough constraints (limited CPU and real-time performance) Shapire (inventor of AdaBoost) has a good course http://www.cs.princeton.edu/~schapire/ Hinton…
on “Ask YC: Machine learning course recommendation.” ↗Obligatory link to "Probabilistic Robotics" -- generally considered the #1 reference on the subject, written by top roboticists (Thrun, Burgard, and Fox): http://www.amazon.com/Probabilistic-Robotics-Intelligent-Aut...
on “Robotic Airplane Flies In Tight Spaces ” ↗…actually invented Kalman filters independently and it turns out they are all actually the same thing, they just have widely varying derivations. My personal favourite explanation comes from the textbook: Probabilistic Robotics (2005) by Thrun, Burgard, and Fox.
on “Show HN: Indoor Location you can try yourself” ↗…community, so the books are pretty lousy and show-offy, often using nonstandard terms and newfangled jargon to explain what is really plain old probability theory with some state space transitions.Thrun's Probabilistic Robotics is the best one I could find. Any math professor can run circles around the material if it were treated as part of math instead of bringing AI into the mix.
on “Ask HN: What's your speciality, and what's your "FizzBuzz" equivalent?” ↗I really like the concepts in Probabilistic Robotics by S. Thrun. It's kind of a more formal approach to robotics, and has some strong ties to ML.
on “Deep Learning for Robotics” ↗Somewhat different to the suggestions so far, but Thrun et. al's 'Probabilistic Robotics' is a very good applied probability text, with a focus on physical systems. Plenty of worked examples and problem sets as well.
on “Ask HN: What are your favorite statistics and probability textbooks?” ↗…which is very elegant and intuitive. Then once you plug in a bunch of Gaussians and linearity in the Bayes filter and crunch the math, a Kalman filter falls out. This is how it's treated in the book Probabilistic Robotics by Thrun, Burgard, Fox and I found it to be one of the best treatments on Kalman filters. A really great book overall.
on “Derive Yourself a Kalman Filter” ↗I also just finished Thinking in Systems and really liked it. I think Feedback Control for Computer Systems[0] is a bit more on the side of a working software engineer (python2 though). Probabilistic Robotics[1] is also an technical approach on control and feedback. [0]: https://www.amazon.com/Feedback-Control-Computer-Systems-Int... [1]: https://www.amazon.com/Probabilistic-Robotics-INTELLIGENT-RO...
on “Ask HN: Which books do you consider real gems in your field of work/study?” ↗If you're interested in perception, like tracking & freespace detection or SLAM, then "Probabilistic Robotics" by Thrun is pretty good. For a more fundamental take on that stuff (but less robotics specific), "Pattern Recognition and Machine Learning" by Bishop is my favorite.
on “Ask HN: Courses/textbook recommendations for studying robotics?” ↗Probabilistic Robotics by Thurn/Burgard/Fox is basically the best resource for anything SLAM or planning related. Artificial Intelligence: A Modern Approach by Russell/Norvig is another good book for planning and AI.
on “Ask HN: Courses/textbook recommendations for studying robotics?” ↗…UIUC and was scrolling by and stopped as I saw two familiar names: Ilan (took Random processes with him and loved it) and of course who in robotics wouldn't know Prof. Thrun (for those who don't, his Probabilistic Robotics is a mandatory reference in every robotics class). Coming to the paper, I loved your blog post, it gave me a gist of everything I wanted to know without reading the paper (I have back ground in bandits).…
on “Show HN: Want something better than k-means? Try BanditPAM” ↗