Survey Paper

International Journal of Control, Automation, and Systems 2023; 21(9): 2771-2781

https://doi.org/10.1007/s12555-023-9902-z

© The International Journal of Control, Automation, and Systems

State Estimation Algorithms for Localization: A Survey

Jung Min Pak and Choon Ki Ahn*

Korea University

Abstract

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.

Keywords Finite impulse response filter, Kalman filter, localization, particle filter, state estimation.

Article

Survey Paper

International Journal of Control, Automation, and Systems 2023; 21(9): 2771-2781

Published online September 1, 2023 https://doi.org/10.1007/s12555-023-9902-z

Copyright © The International Journal of Control, Automation, and Systems.

State Estimation Algorithms for Localization: A Survey

Jung Min Pak and Choon Ki Ahn*

Korea University

Abstract

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.

Keywords: Finite impulse response filter, Kalman filter, localization, particle filter, state estimation.

IJCAS
April 2024

Vol. 22, No. 4, pp. 1105~1460

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