A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle
Joint Authors
Source
Journal of Advanced Transportation
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper presents a method of particle filter localization for autonomous vehicles, based on two-dimensional (2D) laser sensor measurements and road features.
To navigate an urban environment, an autonomous vehicle should be able to estimate its location with a reasonable accuracy.
By detecting road features such as curbs and road markings, a grid-based feature map is constructed using 2D laser range finder measurements.
Then, a particle filter is employed to accurately estimate the position of the autonomous vehicle.
Finally, the performance of the proposed method is verified and compared to accurate Differential Global Positioning Systems (DGPS) data through real road driving experiments.
American Psychological Association (APA)
Ahn, KyungJae& Kang, Yeonsik. 2019. A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1169782
Modern Language Association (MLA)
Ahn, KyungJae& Kang, Yeonsik. A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle. Journal of Advanced Transportation No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1169782
American Medical Association (AMA)
Ahn, KyungJae& Kang, Yeonsik. A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1169782
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169782