Event-Based Photometric Gaussian Mixture Models for Visual Servoing

This paper presents a novel approach for visual servoing using neuromorphic event-based cameras. We extend the photometric Gaussian mixture model framework from frame-based to event-based vision by developing a mathematical formulation that bridges conventional models with the sparse, temporally-precise nature of event data. Our method transforms raw event streams into effective visual features through time surface representations, enabling visual servoing that leverages the microsecond temporal resolution and high dynamic range of event cameras. Evaluation using the N-Caltech101 dataset demonstrates excellent convergence characteristics and a high success rate (96.9%) across diverse object categories. Results confirm that our event-based photometric Gaussian mixture approach effectively exploits the temporal precision of event cameras while providing reliable performance for robot control tasks.