EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
Accurate object registration is crucial for precise localization and environment sensing in autonomous driving systems. While real-time sensors such as cameras and radar capture the local environment, high-definition (HD) maps provide a global reference frame that enhances localization accuracy and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/66 |
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| author | Nian Hui Zijie Jiang Zhongliang Cai Shen Ying |
| author_facet | Nian Hui Zijie Jiang Zhongliang Cai Shen Ying |
| author_sort | Nian Hui |
| collection | DOAJ |
| description | Accurate object registration is crucial for precise localization and environment sensing in autonomous driving systems. While real-time sensors such as cameras and radar capture the local environment, high-definition (HD) maps provide a global reference frame that enhances localization accuracy and robustness, especially in complex scenarios. In this paper, we propose an innovative method called enhanced object registration (EOR) to improve the accuracy and robustness of object registration between camera images and HD maps. Our research investigates the influence of spatial distribution factors and spatial structural characteristics of objects in visual perception and HD maps on registration accuracy and robustness. We specifically focus on understanding the varying importance of different object types and the constrained dimensions of pose estimation. These factors are integrated into a nonlinear optimization model and extended Kalman filter framework. Through comprehensive experimentation on the open-source Argoverse 2 dataset, the proposed EOR demonstrates the ability to maintain high registration accuracy in lateral and elevation dimensions, improve longitudinal accuracy, and increase the probability of successful registration. These findings contribute to a deeper understanding of the relationship between sensing data and scenario understanding in object registration for vehicle localization. |
| format | Article |
| id | doaj-art-8c342522412e411b8ccd1e919a41f312 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-8c342522412e411b8ccd1e919a41f3122025-01-10T13:20:07ZengMDPI AGRemote Sensing2072-42922024-12-011716610.3390/rs17010066EOR: An Enhanced Object Registration Method for Visual Images and High-Definition MapsNian Hui0Zijie Jiang1Zhongliang Cai2Shen Ying3School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaAccurate object registration is crucial for precise localization and environment sensing in autonomous driving systems. While real-time sensors such as cameras and radar capture the local environment, high-definition (HD) maps provide a global reference frame that enhances localization accuracy and robustness, especially in complex scenarios. In this paper, we propose an innovative method called enhanced object registration (EOR) to improve the accuracy and robustness of object registration between camera images and HD maps. Our research investigates the influence of spatial distribution factors and spatial structural characteristics of objects in visual perception and HD maps on registration accuracy and robustness. We specifically focus on understanding the varying importance of different object types and the constrained dimensions of pose estimation. These factors are integrated into a nonlinear optimization model and extended Kalman filter framework. Through comprehensive experimentation on the open-source Argoverse 2 dataset, the proposed EOR demonstrates the ability to maintain high registration accuracy in lateral and elevation dimensions, improve longitudinal accuracy, and increase the probability of successful registration. These findings contribute to a deeper understanding of the relationship between sensing data and scenario understanding in object registration for vehicle localization.https://www.mdpi.com/2072-4292/17/1/66high-definition mapregistrationvisual localizationautonomous drivingcross modality |
| spellingShingle | Nian Hui Zijie Jiang Zhongliang Cai Shen Ying EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps Remote Sensing high-definition map registration visual localization autonomous driving cross modality |
| title | EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps |
| title_full | EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps |
| title_fullStr | EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps |
| title_full_unstemmed | EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps |
| title_short | EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps |
| title_sort | eor an enhanced object registration method for visual images and high definition maps |
| topic | high-definition map registration visual localization autonomous driving cross modality |
| url | https://www.mdpi.com/2072-4292/17/1/66 |
| work_keys_str_mv | AT nianhui eoranenhancedobjectregistrationmethodforvisualimagesandhighdefinitionmaps AT zijiejiang eoranenhancedobjectregistrationmethodforvisualimagesandhighdefinitionmaps AT zhongliangcai eoranenhancedobjectregistrationmethodforvisualimagesandhighdefinitionmaps AT shenying eoranenhancedobjectregistrationmethodforvisualimagesandhighdefinitionmaps |