Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes
LiDAR-based localization has been widely used for the pose estimation of autonomous vehicles. Since the localization requires a sustainable map reflecting environment changes, a map update framework based on crowd-sourcing measurements has been researched. Unfortunately, a point cloud map occupies t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/4486177 |
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| _version_ | 1832562339401957376 |
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| author | Chansoo Kim Sungjin Cho Myoungho Sunwoo Paulo Resende Benazouz Bradaï Kichun Jo |
| author_facet | Chansoo Kim Sungjin Cho Myoungho Sunwoo Paulo Resende Benazouz Bradaï Kichun Jo |
| author_sort | Chansoo Kim |
| collection | DOAJ |
| description | LiDAR-based localization has been widely used for the pose estimation of autonomous vehicles. Since the localization requires a sustainable map reflecting environment changes, a map update framework based on crowd-sourcing measurements has been researched. Unfortunately, a point cloud map occupies too large data size to transmit data in the uploading and downloading of the map update framework. To realize the LiDAR map update framework by reducing the data size, we proposed a novel map update framework using a Geodetic Normal Distribution (GND) map that compresses the point cloud to the normal distributions. The proposed GND map update framework comprises two parts: map change detection based on crowd-sourcing vehicles and map updating based on a map cloud server. GND map changes are detected based on an evidence theory considering geometric relationships between the GND map and crowd-sourcing measurements and uploaded to the map cloud server. Uploaded map changes reproduce representative map changes based on a similarity-based clustering, which are updated into the GND map. The proposed framework was evaluated in simulations and real environments on construction sites. As a result, although partial map changes occurred, the GND map was kept up-to-date through the proposed framework and the localization for autonomous driving was performed successfully. |
| format | Article |
| id | doaj-art-daacb0cbfc5d45cfbfc2c9a31d0281b1 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-daacb0cbfc5d45cfbfc2c9a31d0281b12025-02-03T01:22:57ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/4486177Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment ChangesChansoo Kim0Sungjin Cho1Myoungho Sunwoo2Paulo Resende3Benazouz Bradaï4Kichun Jo5Department of Intelligent MobilitySmart Mobility R&D CenterDepartment of Automotive ConvergenceDriving Assistance ResearchDriving Assistance ResearchDepartment of Smart Vehicle EngineeringLiDAR-based localization has been widely used for the pose estimation of autonomous vehicles. Since the localization requires a sustainable map reflecting environment changes, a map update framework based on crowd-sourcing measurements has been researched. Unfortunately, a point cloud map occupies too large data size to transmit data in the uploading and downloading of the map update framework. To realize the LiDAR map update framework by reducing the data size, we proposed a novel map update framework using a Geodetic Normal Distribution (GND) map that compresses the point cloud to the normal distributions. The proposed GND map update framework comprises two parts: map change detection based on crowd-sourcing vehicles and map updating based on a map cloud server. GND map changes are detected based on an evidence theory considering geometric relationships between the GND map and crowd-sourcing measurements and uploaded to the map cloud server. Uploaded map changes reproduce representative map changes based on a similarity-based clustering, which are updated into the GND map. The proposed framework was evaluated in simulations and real environments on construction sites. As a result, although partial map changes occurred, the GND map was kept up-to-date through the proposed framework and the localization for autonomous driving was performed successfully.http://dx.doi.org/10.1155/2022/4486177 |
| spellingShingle | Chansoo Kim Sungjin Cho Myoungho Sunwoo Paulo Resende Benazouz Bradaï Kichun Jo Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes Journal of Advanced Transportation |
| title | Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes |
| title_full | Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes |
| title_fullStr | Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes |
| title_full_unstemmed | Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes |
| title_short | Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes |
| title_sort | cloud update of geodetic normal distribution map based on crowd sourcing detection against road environment changes |
| url | http://dx.doi.org/10.1155/2022/4486177 |
| work_keys_str_mv | AT chansookim cloudupdateofgeodeticnormaldistributionmapbasedoncrowdsourcingdetectionagainstroadenvironmentchanges AT sungjincho cloudupdateofgeodeticnormaldistributionmapbasedoncrowdsourcingdetectionagainstroadenvironmentchanges AT myounghosunwoo cloudupdateofgeodeticnormaldistributionmapbasedoncrowdsourcingdetectionagainstroadenvironmentchanges AT pauloresende cloudupdateofgeodeticnormaldistributionmapbasedoncrowdsourcingdetectionagainstroadenvironmentchanges AT benazouzbradai cloudupdateofgeodeticnormaldistributionmapbasedoncrowdsourcingdetectionagainstroadenvironmentchanges AT kichunjo cloudupdateofgeodeticnormaldistributionmapbasedoncrowdsourcingdetectionagainstroadenvironmentchanges |