Improved ORB-SLAM2 Algorithm with Image deblurring

Liu Peiqi

doi:10.15002/00025366

ORB-SLAM2 is a feature-based sparse visual simultaneous localization and mapping (SLAM) system. After obtaining images and related data from sensors and cameras, it can extract useful information from them, and perform real-time positioning and map construction. This technology is now widely used in the fields of unmanned driving, smart home and factory operations, and it also provides a relatively stable foundation for the creation and development of many new SLAM systems. However, the current ORB-SLAM2 system can only work stably and normally when the camera is moving steadily and slowly. When the camera moves too fast or violently bumps, the image taken by the camera will inevitably be blurred, which will easily cause the "Track Lost" of the ORB-SLAM2 system. In order to solve this problem, this thesis combines the ORB-SLAM2 system with a deblurring algorithm based on deep learning, and uses the SLAM-oriented data set to optimize the deep learning network. This thesis also adds a blur detection module to ORB-SLAM2, which enables it to automatically distinguish blurry images. The experimental results demonstrated that this work detects and deblurs the blurred image, successfully avoiding part of the "Track Lost", and improving the accuracy of ORB-SLAM2 mapping.

Improved ORB-SLAM2 Algorithm with Image deblurring

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