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.
|Number of pages
|International Journal of Control, Automation and Systems
|Published - 2023 Sept
Bibliographical noteFunding Information:
This work was supported by the Korea National University of Transportation in 2023.
© 2023, ICROS, KIEE and Springer.
- Finite impulse response filter
- Kalman filter
- particle filter
- state estimation
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications