Towards robust visual odometry by motion blur recovery - Luan, Simin; Yang, Cong; Qin, Xue; Chen, Dongfeng; Sui, Wei (Frontiers in Signal Processing, 2024)
Motion blur, primarily caused by rapid camera movements, significantly challenges the robustness of feature point tracking in visual odometry (VO). This paper introduces a robust and efficient approach for motion blur detection and recovery in blur-prone environments (e.g., with rapid movements and uneven terrains). Notably, the Inertial Measurement Unit (IMU) is utilized for motion blur detection, followed by a blur selection and restoration strategy within the motion frame sequence. It marks a substantial improvement over traditional visual methods (typically slow and less effective, falling short in meeting VO's real-time performance demands). To address the scarcity of datasets catering to the image blurring challenge in VO, we also present the BlurVO dataset. This publicly available dataset is richly annotated and encompasses diverse blurred scenes, providing an ideal environment for motion blur evaluation. Our methodology demonstrates a substantial enhancement in robustness and maintains excellent real-time performance: it significantly reduces the percentage of dropped frames in VO, from nearly 100% to just 20%. Moreover, our process, requiring only 20 ms per frame, proves its efficacy on a Jetson Nano, emphasizing its suitability for real-time robotic applications.is compatible with various IMU sensors, enhancing its applicability. For the lack of dataset challenge, we introduce a new publicly available dataset, BlurVO, for indoor and outdoor motion blur evaluations in VO. Comprising 12 sequences from various real-world environments, BlurVO is equipped with data from pre-calibrated cameras and IMUs, fostering the development of more robust algorithms for VO in blur-prone scenarios.Our main contributions are as follows: (1) We introduce a simple yet efficient approach for motion blur detection and restoration based on IMU. It marks a substantial improvement over traditional visual methods in terms of real-time performance, high-accurate blur estimation and recovery, and robustness of VO in