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ORIGINAL RESEARCH article

Front. Signal Process.
Sec. Image Processing
Volume 4 - 2024 | doi: 10.3389/frsip.2024.1417363
This article is part of the Research Topic Efficient Algorithms for Bird's Eye View-based Perception View all articles

Towards Robust Visual Odometry by Motion Blur Recovery

Provisionally accepted
Simin Luan Simin Luan 1Cong Yang Cong Yang 2*Xue Qin Xue Qin 2Dongfeng Chen Dongfeng Chen 2Wei Sui Wei Sui 2
  • 1 Soochow University, Shilin District, Taipei County, Taiwan
  • 2 Suzhou University, Suzhou, Anhui, China

The final, formatted version of the article will be published soon.

    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

    Keywords: SLAM (Simultaneous Localization and Mapping), Deblur, multimodal fusion, Motion blur, IMU

    Received: 14 Apr 2024; Accepted: 26 Jul 2024.

    Copyright: © 2024 Luan, Yang, Qin, Chen and Sui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Cong Yang, Suzhou University, Suzhou, 234000, Anhui, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.