Machine learningMapping and Localization

Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is the problem of enabling a mobile robot to build a map of its environment while simultaneously determining its own location within that map using noisy sensor measurements. Formulated by Durrant-Whyte and Bailey in 2006, SLAM is fundamental to autonomous robotics, enabling robots to navigate and explore unknown environments without prior maps or external positioning systems.

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Sources

  1. Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping (SLAM): Part I. IEEE Robotics & Automation Magazine, 13(2), 99-110. DOI: 10.1109/MRA.2006.1638022
  2. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. MIT Press. link
  3. Dellaert, F., & Kaess, M. (2012). Square root SAM: Simultaneous localization and mapping via square root factor graphs. International Journal of Robotics Research, 25(12), 1181-1203. DOI: 10.1177/0278364906072952

Related methods

ScholarGateSimultaneous Localization and Mapping (Simultaneous Localization and Mapping). Retrieved 2026-06-04 from https://scholargate.app/en/control-theory/simultaneous-localization-and-mapping