State Estimation Algorithms for Localization: A Survey

• Published in: International journal of control, automation, and systems Vol. 21; no. 9; pp. 2771 - 2781
• Main Authors: Pak, Jung Min, Ahn, Choon Ki
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.09.2023; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Algorithms; Autonomous vehicles; Control; Engineering; Localization; Mechatronics; Obstacle avoidance; Regular Papers; Robot control; Robotics; State estimation; Unmanned vehicles; 제어계측공학; localization; Finite impulse response filter; state estimation; particle filter; Kalman filter
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-023-9902-z

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.