Accelerating learned descriptor generation for visual localization - Liu, Woon Kit (Nanyang Technological University, 2024)
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.