Vehicle pose estimation algorithm for parking automated guided vehicle
Parking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are req...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2020-01-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/1729881419891335 |
| _version_ | 1818240735761137664 |
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| author | Zhixiong Ning Xin Wang Jun Wang Huafeng Wen |
| author_facet | Zhixiong Ning Xin Wang Jun Wang Huafeng Wen |
| author_sort | Zhixiong Ning |
| collection | DOAJ |
| description | Parking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are required to be mounted at the top of the vehicle. However, the sensors are always mounted at a lower place because the height of a parking automated guided vehicle is always beyond 0.2mm, where we can only get the vehicle wheel information and limited vehicle body information. In this article, a novel method is given based on the symmetry of wheel point clouds collected by 3-D lidar. Firstly, we combine cell-based method with support vector machine classifier to segment ground point clouds. Secondly, wheel point clouds are segmented from obstacle point clouds and their symmetry are corrected by iterative closest point algorithm. Then, we estimate the vehicle pose by the symmetry plane of wheel point clouds. Finally, we compare our method with registration method that combines sample consensus initial alignment algorithm and iterative closest point algorithm. The experiments have been carried out. |
| first_indexed | 2024-12-12T13:18:10Z |
| format | Article |
| id | doaj.art-54045e46c74a40f0b4670d26d6708d9b |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-12-12T13:18:10Z |
| publishDate | 2020-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-54045e46c74a40f0b4670d26d6708d9b2022-12-22T00:23:22ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142020-01-011710.1177/1729881419891335Vehicle pose estimation algorithm for parking automated guided vehicleZhixiong Ning0Xin Wang1Jun Wang2Huafeng Wen3 School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, People’s Republic of China School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, People’s Republic of China Shenzhen Fine Automation Co. Ltd, Shenzhen, People’s Republic of China Shenzhen Fine Automation Co. Ltd, Shenzhen, People’s Republic of ChinaParking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are required to be mounted at the top of the vehicle. However, the sensors are always mounted at a lower place because the height of a parking automated guided vehicle is always beyond 0.2mm, where we can only get the vehicle wheel information and limited vehicle body information. In this article, a novel method is given based on the symmetry of wheel point clouds collected by 3-D lidar. Firstly, we combine cell-based method with support vector machine classifier to segment ground point clouds. Secondly, wheel point clouds are segmented from obstacle point clouds and their symmetry are corrected by iterative closest point algorithm. Then, we estimate the vehicle pose by the symmetry plane of wheel point clouds. Finally, we compare our method with registration method that combines sample consensus initial alignment algorithm and iterative closest point algorithm. The experiments have been carried out.https://doi.org/10.1177/1729881419891335 |
| spellingShingle | Zhixiong Ning Xin Wang Jun Wang Huafeng Wen Vehicle pose estimation algorithm for parking automated guided vehicle International Journal of Advanced Robotic Systems |
| title | Vehicle pose estimation algorithm for parking automated guided vehicle |
| title_full | Vehicle pose estimation algorithm for parking automated guided vehicle |
| title_fullStr | Vehicle pose estimation algorithm for parking automated guided vehicle |
| title_full_unstemmed | Vehicle pose estimation algorithm for parking automated guided vehicle |
| title_short | Vehicle pose estimation algorithm for parking automated guided vehicle |
| title_sort | vehicle pose estimation algorithm for parking automated guided vehicle |
| url | https://doi.org/10.1177/1729881419891335 |
| work_keys_str_mv | AT zhixiongning vehicleposeestimationalgorithmforparkingautomatedguidedvehicle AT xinwang vehicleposeestimationalgorithmforparkingautomatedguidedvehicle AT junwang vehicleposeestimationalgorithmforparkingautomatedguidedvehicle AT huafengwen vehicleposeestimationalgorithmforparkingautomatedguidedvehicle |