Learning a Robust Hybrid Descriptor for Robot Visual Localization
Long-term robust visual localization is one of the main challenges of long-term visual navigation for mobile robots. Due to factors such as illumination, weather, and season, mobile robots continuously navigate with visual information in a complex scene, which is likely to lead to failure localizati...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2022/9354909 |
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| _version_ | 1850209937357012992 |
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| author | Qingwu Shi Junjun Wu Zeqin Lin Ningwei Qin |
| author_facet | Qingwu Shi Junjun Wu Zeqin Lin Ningwei Qin |
| author_sort | Qingwu Shi |
| collection | DOAJ |
| description | Long-term robust visual localization is one of the main challenges of long-term visual navigation for mobile robots. Due to factors such as illumination, weather, and season, mobile robots continuously navigate with visual information in a complex scene, which is likely to lead to failure localization within a few hours. However, semantic segmentation images will be more stable than the original images against considerable drastically variable environments; therefore, to make full use of the advantages of both semantic segmentation image and its original image, this paper solves the above problems with the latest work of semantic segmentation and proposes the novel hybrid descriptor for long-term visual localization, which is generated by combining a semantic image descriptor extracted from segmentation images and an image descriptor extracted from RGB images with a certain weight, and then trained by a convolutional neural network. Our experiments show that the localization performance of our method combining the advantages of semantic image descriptor and image descriptor is superior to those long-term visual localization methods with only an image descriptor or semantic image descriptor. Finally, our experimental results mostly exceed state-of-the-art 2D image-based localization methods under various challenging environmental conditions in the Extended CMU Seasons and RobotCar Seasons datasets in specific precision metrics. |
| format | Article |
| id | doaj-art-920ac0dde6de4e5b8ebf678c732bbe2f |
| institution | OA Journals |
| issn | 1687-9619 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-920ac0dde6de4e5b8ebf678c732bbe2f2025-08-20T02:09:52ZengWileyJournal of Robotics1687-96192022-01-01202210.1155/2022/9354909Learning a Robust Hybrid Descriptor for Robot Visual LocalizationQingwu Shi0Junjun Wu1Zeqin Lin2Ningwei Qin3School of Mechatronic Engineering and AutomationSchool of Mechatronic Engineering and AutomationSchool of Mechatronic Engineering and AutomationSchool of Mechatronic Engineering and AutomationLong-term robust visual localization is one of the main challenges of long-term visual navigation for mobile robots. Due to factors such as illumination, weather, and season, mobile robots continuously navigate with visual information in a complex scene, which is likely to lead to failure localization within a few hours. However, semantic segmentation images will be more stable than the original images against considerable drastically variable environments; therefore, to make full use of the advantages of both semantic segmentation image and its original image, this paper solves the above problems with the latest work of semantic segmentation and proposes the novel hybrid descriptor for long-term visual localization, which is generated by combining a semantic image descriptor extracted from segmentation images and an image descriptor extracted from RGB images with a certain weight, and then trained by a convolutional neural network. Our experiments show that the localization performance of our method combining the advantages of semantic image descriptor and image descriptor is superior to those long-term visual localization methods with only an image descriptor or semantic image descriptor. Finally, our experimental results mostly exceed state-of-the-art 2D image-based localization methods under various challenging environmental conditions in the Extended CMU Seasons and RobotCar Seasons datasets in specific precision metrics.http://dx.doi.org/10.1155/2022/9354909 |
| spellingShingle | Qingwu Shi Junjun Wu Zeqin Lin Ningwei Qin Learning a Robust Hybrid Descriptor for Robot Visual Localization Journal of Robotics |
| title | Learning a Robust Hybrid Descriptor for Robot Visual Localization |
| title_full | Learning a Robust Hybrid Descriptor for Robot Visual Localization |
| title_fullStr | Learning a Robust Hybrid Descriptor for Robot Visual Localization |
| title_full_unstemmed | Learning a Robust Hybrid Descriptor for Robot Visual Localization |
| title_short | Learning a Robust Hybrid Descriptor for Robot Visual Localization |
| title_sort | learning a robust hybrid descriptor for robot visual localization |
| url | http://dx.doi.org/10.1155/2022/9354909 |
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