State Estimation Algorithms for Localization: A Survey

Jung Min Pak, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Unmanned vehicles represent a research hotspot in the fields of control and robotics. The realization of autonomous driving of unmanned vehicles requires various technologies, such as localization, mapping, path planning, and obstacle avoidance. Among these technologies, localization is a fundamental component, which can be accomplished through various methods. In this work, we focus on localization based on state estimation, as these algorithms are predominantly applied to unmanned vehicles. This paper provides a comprehensive review of state estimation algorithms commonly used for the localization of unmanned vehicles, from the perspective of control and robotic engineers. First, we provide an overview of localization schemes based on state estimation algorithms. Subsequently, we can categorize the research subjects into eight classes and clarify the principles and features of each type of state estimation algorithm. Furthermore, we examine the recent research trends associated with these algorithms.

Original languageEnglish
Pages (from-to)2771-2781
Number of pages11
JournalInternational Journal of Control, Automation and Systems
Issue number9
Publication statusPublished - 2023 Sept

Bibliographical note

Funding Information:
This work was supported by the Korea National University of Transportation in 2023.

Publisher Copyright:
© 2023, ICROS, KIEE and Springer.


  • Finite impulse response filter
  • Kalman filter
  • localization
  • particle filter
  • state estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


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