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State Estimation Algorithms for Localization: A Survey

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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.

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Authors and Affiliations

  1. Department of AI Data Engineering, Korea National University of Transportation, Gyeonggi-do, Uiwang-si, 16106, Korea

    Jung Min Pak

  2. School of Electrical Engineering, Korea University, Seoul, 02841, Korea

    Choon Ki Ahn

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

Jung Min Pak received his B.S., M.S., and Ph.D. degrees in the School of Electrical Engineering from Korea University, Seoul, Korea, in 2006, 2008, and 2015, respectively. From 2016 to 2020, he was an Assistant Professor with the Department of Electrical Engineering, Wonkwang University, Iksan, Korea. From 2020 to 2023, he was an Associate Professor with the Department of Electrical Engineering, Wonkwang University. He is currently an Associate Professor with the Department of AI Data Engineering, Korea National University of Transportation, Gyeonggi-do, Uiwang-si, Korea. His research interests include state estimation, target tracking, and localization.

Choo Ki Ahn received his B.S. and M.S. degrees in the School of Electrical Engineering from Korea University, Seoul, Korea, in 2000 and 2002, respectively. He received a Ph.D. degree in the School of Electrical Engineering and Computer Science from Seoul National University, Seoul, Korea, in 2006. He is currently a Crimson Professor of Excellence with the College of Engineering and a Full Professor with the School of Electrical Engineering, Korea University, Seoul, Korea. He was the recipient of the Early Career Research Award of Korea University in 2015. In 2016, he was ranked #1 in Electrical/Electronic Engineering among Korean young professors based on research quality. In 2017, he received the Presidential Young Scientist Award from the President of Korea. In 2020, he received the Outstanding Associate Editor Award for IEEE Transactions on Neural Networks and Learning Systems. In 2021, he received the Best Associate Editor Award for IEEE Transactions on Systems, Man, and Cybernetics: Systems. In 2019–2023, he received the Research Excellence Award from Korea University (Top 3% Professor of Korea University in Research). He has been a Senior Editor of IEEE Transactions on Neural Networks and Learning Systems; IEEE Systems Journal and also an Associate Editor of IEEE Transactions on Fuzzy Systems; IEEE Transactions on Systems, Man, and Cybernetics: Systems; IEEE Transactions on Automation Science and Engineering; IEEE Transactions on Intelligent Transportation Systems; IEEE Transactions on Circuits and Systems I: Regular Papers; IEEE Systems, Man, and Cybernetics Magazine; Nonlinear Dynamics; Aerospace Science and Technology; and other flagship journals. He is the recipient of the 2019–2022 Highly Cited Researcher Award in Engineering by Clarivate Analytics (formerly, Thomson Reuters).

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Pak, J.M., Ahn, C.K. State Estimation Algorithms for Localization: A Survey. Int. J. Control Autom. Syst. 21, 2771–2781 (2023). https://doi.org/10.1007/s12555-023-9902-z

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