Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146492
Title: Survey on localization systems and algorithms for unmanned systems
Authors: Yuan, Shenghai
Wang, Han
Xie, Lihua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Yuan, S., Wang, H., & Xie, L. (2021). Survey on localization systems and algorithms for unmanned systems. Unmanned Systems. doi:10.1142/S230138502150014X
Journal: Unmanned Systems
Abstract: Intelligent unmanned systems have important applications, such as pesticide-spraying in agriculture, robot-based warehouse management systems, and missile-firing drones. The underlying assumption behind all autonomy is that the agent knows its relative position or egomotion with respect to some reference or scene. There exist thousands of localization systems in the literature. These localization systems use various combinations of sensors and algorithms, such as visual/visual-inertial SLAM, to achieve robust localization. The majority of the methods use one or more sensors from LIDAR, camera, IMU, UWB, GPS, compass, tracking system, etc. This survey presents a systematic review and analysis of published algorithms and techniques chronologically, and we introduce various highly impactful works. We provide insightful investigation and taxonomy on sensory data forming principle, feature association principle, egomotion estimation formation, and fusion model for each type of system. At last, some open problems and directions for future research are also included. We aim to survey the literature comprehensively to provide a complete understanding of localization methodologies, performance, advantages and limitations, and evaluations of various methods, shedding some light for future research.
URI: https://hdl.handle.net/10356/146492
ISSN: 2301-3850
DOI: 10.1142/S230138502150014X
Rights: Electronic version of an article published as [Unmanned Systems, 2021] https://doi.org/10.1142/S230138502150014X] @ copyright World Scientific Publishing Company [https://www.worldscientific.com/worldscinet/us].
Fulltext Permission: embargo_20220212
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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