On Motion Blur and Deblurring in Visual Place Recognition

TL;DR

This paper investigates the impact of motion blur on visual place recognition (VPR), introduces a benchmark for evaluation, and proposes adaptive deblurring strategies to improve VPR performance under motion blur conditions.

Contribution

It introduces a new benchmark for evaluating VPR under motion blur, and proposes adaptive deblurring strategies to enhance VPR robustness in real-world scenarios.

Findings

Motion blur significantly affects VPR accuracy.

Image deblurring can improve VPR performance.

Adaptive deblurring strategies offer effective mitigation.

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 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.