State Estimation Algorithms for Localization: A Survey

Citations

WEB OF SCIENCE

22
Citations

SCOPUS

26

초록

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.

키워드

Finite impulse response filter; Kalman filter; localization; particle filter; state estimation; MONTE-CARLO LOCALIZATION; ROBUST KALMAN FILTER; PARTICLE FILTERS; SYSTEMS
제목
State Estimation Algorithms for Localization: A Survey
저자
Pak, Jung Min; Ahn, Choon Ki
DOI
10.1007/s12555-023-9902-z
발행일
2023-08-12
유형
Article
저널명
International Journal of Control, Automation, and Systems
권
21
호
9
페이지
2771 ~ 2781