On Motion Blur and Deblurring in Visual Place Recognition
Timur Ismagilov, Bruno Ferrarini, Michael J Milford, Nguyen Tan Viet Tuyen, Sarvapali Ramchurn, Shoaib Ehsan
AI summary
Problem
The impact of motion blur on Visual Place Recognition remains underexplored due to a lack of comprehensive benchmarks, and the effectiveness of image deblurring as a mitigation strategy in real-world scenarios is poorly understood.
Approach
The authors introduce the 'Blurry Places' benchmark featuring controlled motion blur intensities across outdoor scenes, systematically evaluating state-of-the-art VPR methods and deblurring techniques to propose adaptive blur mitigation strategies.
Key results
- Introduced the 'Blurry Places' benchmark with controllable motion blur and appearance variations
- Demonstrated severe VPR performance degradation under blur, with FloppyNet and MixVPR showing superior robustness
- Showed that offline deblurring improves accuracy but favors adaptive strategies for computational efficiency
- Established that combined blur and environmental variations drastically reduce VPR reliability
Why it matters
Provides a critical evaluation framework and practical guidelines for developing robust VPR systems in real-world mobile robotics and autonomous navigation where motion blur is unavoidable.
Abstract
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 im- pact 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.