Accelerating learned descriptor generation for visual localization
Visual SLAM systems use traditional feature extractor to retrieve features, a pair consisting of a keypoint and descriptor, from images. These features can then be matched to estimate the camera pose. However, these traditional feature extractors are surpassed by newer deep learning-based feature ex...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/175279 |
| الوسوم: |
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| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | Visual SLAM systems use traditional feature extractor to retrieve features, a pair consisting of a keypoint and descriptor, from images. These features can then be matched to estimate the camera pose. However, these traditional feature extractors are surpassed by newer deep learning-based feature extractor in the presence of imaging noise, illumination, or viewpoint changes. However, such AI models may suffer performance issues when deployed to embedded devices, which prioritises low-powered consumption. This report investigates the potential of deep learning accelerator libraries to accelerate feature extractor models for application in visual SLAM systems, particularly on embedded devices. TensorRT, is such a library that this can help achieve a significant speedup compared to traditional feature extraction methods. |
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