Event-Based Ratslam: Enhancing Ratslam with Event-Aligned Velocity Estimation and Kalman Filtering for Mapping

Inspired by how biological visual systems detect local changes in brightness, event-based cameras achieve high temporal resolution and low latency. This paper introduces a bio-inspired event-based Simultaneous Localization and Mapping (SLAM), demonstrating event-based cameras' potential for environmental understanding tasks. Event-based RatSLAM is a bio-inspired SLAM technique that utilizes a computational model of a rodent's hippocampus to navigate unknown environments by processing events, effectively replicating rodent spatial mapping strategies. The proposed framework adapts the RatSLAM framework to apply event-driven sensory inputs to derive odometric data instead of intensity-based image inputs. Translational and angular velocities are estimated through an optimal event alignment approach using a Poisson Point Process (PPP). Then, the linear velocity estimation is further refined using a Kalman Filter. The performance of the proposed framework is validated using real-world datasets captured with a DVXplorer event-based camera.