Abstract
Real-time and robust long-term visual localization is a key technology for autonomous driving. Season and illumination variance, as well as limited computing power make this problem more challenging. At present, most of the excellent visual localization algorithms cannot run in real-time on devices with limited computing power. In this paper, we propose SAMLoc, a self-supervised 6-DoF visual localization method with structure-aware and multi-task distillation. We integrate the structure-aware constraints into the hierarchical localization network of multi-task distillation, which greatly reduces the feature extraction time while ensuring localization accuracy, thus achieving real-time and robust large-scene localization on mobile devices. Our method takes both speed and accuracy into consideration, and extensive experiments have been conducted to validate the effectiveness of the proposed approach on several datasets. Our network is not only lightweight but also has excellent generalization ability, and still exhibits high localization accuracy even with challenging datasets.
Original language | English |
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Title of host publication | Proceedings of International Conference on Robotics and Automation 2023 |
Publisher | IEEE |
Publication status | Accepted/In press - 17 Jan 2023 |
Event | International Conference on Robotics and Automation 2023 - London, England, London, United Kingdom Duration: 29 May 2023 → 2 Jun 2023 https://www.icra2023.org/ |
Conference
Conference | International Conference on Robotics and Automation 2023 |
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Abbreviated title | ICRA 2023 |
Country/Territory | United Kingdom |
City | London |
Period | 29/05/23 → 2/06/23 |
Internet address |
Keywords
- Visual localization
- Light-weight networks
- Generalisation