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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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Presentation Transcript
localization
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
Mapping

Perfect Localization

+

Observations with errors

=

Pretty good map

(Average out errors, add new observations to the map)

slide4

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
slide5

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

slide6
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.
how do we calculate those probabilities
How do we calculate those probabilities?
  • Kalman Filters
  • Particle Filters
  • Bayesian networks
references
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