MBRVO: A Blur Robust Visual Odometry Based on Motion Blurred Artifact Prior

How to estimate camera pose from motion-blurred images remains a challenge for visual odometry. The blurring artifacts are inevitably caused by the exposure during camera motion. While current visual odometry regards them as noise, we argue that it is necessary to extract potential information from blur artifacts, as they contain prior knowledge of camera motion. Base on this, we propose a blur-robust visual odometry that improves the accuracy of camera pose estimation through exposure trajectory. Specifically, we first use the exposure trajectory to guide pixel matching between neighboring frames. The blur mask is then generated based on the magnitude of the exposure trajectory. The mask makes the pose module pay less attention to the feature information in the severely blurred regions. Experiments show that our proposed end-to-end visual odometry achieves competitive performance on most sequences of motion blurred datasets.