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Simultaneous Localization and Mapping

Simultaneous Localization and Mapping. (SLAM). Localization. Perfect Map + Observations with errors = Pretty good Localization (Average out errors in observations, look for a place on the map that matches our observations). Mapping. Perfect Localization + Observations with errors =

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Simultaneous Localization and Mapping

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  1. Simultaneous Localization and Mapping (SLAM)

  2. Localization Perfect Map + Observations with errors = Pretty good Localization (Average out errors in observations, look for a place on the map that matches our observations)

  3. Mapping Perfect Localization + Observations with errors = Pretty good map (Average out errors, add new observations to the map)

  4. Perfect Map Perfect Localization Unfortunately, we don’t usually have either of these. • Often, we want to build the map as we go • GPS isn’t perfect, wheel rotation sensors aren’t either

  5. http://robotics.jacobs-university.de/sites/default/files/images/mappingfinal-back-kob.jpghttp://robotics.jacobs-university.de/sites/default/files/images/mappingfinal-back-kob.jpg

  6. SLAM • How do we estimate localization and map at the same time? • Explore multiple (path, map) possibilities • For each, keep track of: • After each update, return the (path, map) combination with the highest probability.

  7. How do we calculate those probabilities? • Kalman Filters • Particle Filters • Bayesian networks

  8. References • Russel, Stuart. Norvig, Peter. Artifical Intelligence: A Modern Approach • Chapter 15 talks about kalman filters, etc. • Chapter 25 talks about SLAM • Thrun, Sebastian, et. all. Probabilistic Robotics • Slides at http://www.probabilistic-robotics.org/ • Thrun, Sebastian. Videos and Animations. http://robots.stanford.edu/videos.html

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