A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles
Ensuring that a vehicle can obtain its real location in a high-precision prebuilt map is one of the most important tasks of the Autonomous Internet of Vehicles (AIoV). In this work, we show that the resampling of the particle filter (PF) algorithm is optimized by using the prior information of parti...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/12/1816 |
| _version_ | 1797488039264518144 |
|---|---|
| author | Qian Chen Xin Tang Zhaoyu Su Xiaohuan Li Duiwu Wang |
| author_facet | Qian Chen Xin Tang Zhaoyu Su Xiaohuan Li Duiwu Wang |
| author_sort | Qian Chen |
| collection | DOAJ |
| description | Ensuring that a vehicle can obtain its real location in a high-precision prebuilt map is one of the most important tasks of the Autonomous Internet of Vehicles (AIoV). In this work, we show that the resampling of the particle filter (PF) algorithm is optimized by using the prior information of particles that shift real localizations to improve vehicle localization accuracy without changing the existing PF process, i.e., the particle shift filter (PSF). The number of particles is critical to their convergence efficiency. We perform quantitative and qualitative analyses of how to improve particle localization accuracy while ensuring timeliness, without increasing the number of particles. Moreover, the cumulative error of the particles increases with time, and the localization accuracy and robustness decrease. Our findings show that the initial particle density is 159 particles/m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>, and the cumulative variance of the PSF particles is improved by 27%, 29%, and 82% at the x-, y-, and z-axes, respectively, under the same conditions as the PF algorithm, while the calculation time only increases by 10.6%. Moreover, the cumulative mean error is reduced by 0.74 m, 0.88 m, and 0.68 m at the x-, y-, and z-axes, respectively, indicating that the localization error of the PSF changes less with time. All experiments were performed using open-source software and datasets. |
| first_indexed | 2024-03-09T23:56:25Z |
| format | Article |
| id | doaj.art-648a7bbdaae64de0b444db6475c42b80 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T23:56:25Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-648a7bbdaae64de0b444db6475c42b802023-11-23T16:24:08ZengMDPI AGElectronics2079-92922022-06-011112181610.3390/electronics11121816A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of VehiclesQian Chen0Xin Tang1Zhaoyu Su2Xiaohuan Li3Duiwu Wang4School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaEnsuring that a vehicle can obtain its real location in a high-precision prebuilt map is one of the most important tasks of the Autonomous Internet of Vehicles (AIoV). In this work, we show that the resampling of the particle filter (PF) algorithm is optimized by using the prior information of particles that shift real localizations to improve vehicle localization accuracy without changing the existing PF process, i.e., the particle shift filter (PSF). The number of particles is critical to their convergence efficiency. We perform quantitative and qualitative analyses of how to improve particle localization accuracy while ensuring timeliness, without increasing the number of particles. Moreover, the cumulative error of the particles increases with time, and the localization accuracy and robustness decrease. Our findings show that the initial particle density is 159 particles/m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>, and the cumulative variance of the PSF particles is improved by 27%, 29%, and 82% at the x-, y-, and z-axes, respectively, under the same conditions as the PF algorithm, while the calculation time only increases by 10.6%. Moreover, the cumulative mean error is reduced by 0.74 m, 0.88 m, and 0.68 m at the x-, y-, and z-axes, respectively, indicating that the localization error of the PSF changes less with time. All experiments were performed using open-source software and datasets.https://www.mdpi.com/2079-9292/11/12/1816localizationpoint cloud mapparticle shiftAutonomous Internet of Vehicles |
| spellingShingle | Qian Chen Xin Tang Zhaoyu Su Xiaohuan Li Duiwu Wang A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles Electronics localization point cloud map particle shift Autonomous Internet of Vehicles |
| title | A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles |
| title_full | A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles |
| title_fullStr | A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles |
| title_full_unstemmed | A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles |
| title_short | A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles |
| title_sort | particle shift prior information fusion localization algorithm for the autonomous internet of vehicles |
| topic | localization point cloud map particle shift Autonomous Internet of Vehicles |
| url | https://www.mdpi.com/2079-9292/11/12/1816 |
| work_keys_str_mv | AT qianchen aparticleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT xintang aparticleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT zhaoyusu aparticleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT xiaohuanli aparticleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT duiwuwang aparticleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT qianchen particleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT xintang particleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT zhaoyusu particleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT xiaohuanli particleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles AT duiwuwang particleshiftpriorinformationfusionlocalizationalgorithmfortheautonomousinternetofvehicles |