提出新基准评估运动模糊对视觉定位的影响并设计自适应去模糊策略。
On Motion Blur and Deblurring in Visual Place Recognition
- 构建三套不同模糊强度的数据集，系统评估模糊影响。
- 实验证明去模糊可显著提升模糊场景下的定位准确率。
- 适合研究机器人视觉定位与图像恢复的工程师和学者。
移动机器人中的视觉定位（VPR）通过视觉数据识别已访问位置实现自我定位。尽管光照、季节、天气和视角变化对VPR可靠性已有广泛研究，但运动模糊的影响仍相对未被充分探索，尤其在快速运动或低光条件下（需长曝光时间）更为关键。同时，图像去模糊在提升模糊场景下VPR性能方面的潜力也尚未得到足够关注。本文通过引入一个新基准，系统评估运动模糊及去模糊对VPR的影响。该基准包含三套涵盖广泛运动模糊强度的数据集，为分析提供全面平台。基于多个成熟VPR与去模糊方法的实验结果，揭示了运动模糊的负面影响及去模糊带来的性能提升。在此基础上，本文提出适用于动态真实场景的自适应去模糊策略，有效应对运动模糊挑战。
原文摘要 · Abstract (English)
Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion blur is relatively unexplored despite its relevance not only in rapid motion scenarios but also in low-light conditions where longer exposure times are necessary. Similarly, the role of image deblurring in enhancing VPR performance under motion blur has received limited attention so far. This paper bridges these gaps by introducing a new benchmark designed to evaluate VPR performance under the influence of motion blur and image deblurring. The benchmark includes three datasets that encompass a wide range of motion blur intensities, providing a comprehensive platform for analysis. Experimental results with several well-established VPR and image deblurring methods provide new insights into the effects of motion blur and the potential improvements achieved through deblurring. Building on these findings, the paper proposes adaptive deblurring strategies for VPR, designed to effectively manage motion blur in dynamic, real-world scenarios.
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