Bringing Loop Closure To Event-based SLAM: A Place Recognition Approach

Event-based cameras excel at capturing visual information with high temporal resolution and low latency, making them well-suited for dynamic environments and lowlight conditions where traditional cameras struggle. Several event-based visual odometry and SLAM methods for real-time motion estimation have been proposed to leverage these benefits. However, most of these approaches lack a loop closure detection mechanism, which is crucial for long-term localization and drift correction. To address this limitation, we propose an Event-Based Place Recognition Module that can integrate with different event-based SLAM frameworks to enhance loop closure detection. We evaluate our approach using event-based camera datasets in combination with a version of the biologically-inspired RatSLAM framework adapted for event-based cameras, demonstrating its ability to detect loop closures effectively.