A Degradation-Robust Keyframe Selection Method Based on Image Quality Evaluation for Visual Localization

Localization information is increasingly crucial for incorporating location context into Internet of Things (IoT) data. As an important task in visual localization, keyframe selection helps effective augmentation of visual odometry. Although considerable progress has been made in the research field of keyframe selection, they have rarely focused on dealing with degraded input sensory data in the real world. To this extent, this work proposes a novel concept by incorporating image quality evaluation into the visual localization so that the keyframe selection module can identify images that may cause undesirable effects and take measures to avoid the catastrophic impact of degraded images. The quality for each image is estimated online using deep classifier trained with the image-itself, image-differential, and external information. Since no model-specific knowledge is needed, our method is applicable to any visual localization system. By creating a challenging data set based on current public data sets under autonomous driving and unmanned aerial vehicles (UAVs) scenarios and using it to evaluate our method, we obtain estimated trajectories that are closer to the original situation while validating its robustness to challenging degraded environments.