6-DOF Pose Estimation For Event Cameras Using A Transformer-Based Approach

Event cameras are novel sensors that provide significant advantages over traditional cameras, such as low latency, high dynamic range, and reduced motion blur.These properties make them particularly well-suited for 6-DOF pose estimation tasks in challenging environments.In this paper, we present a novel transformer-based approach for 6-DOF pose estimation using event camera data.Our method combines a pretrained ResNet50 backbone for feature extraction with a custom transformer encoder to model the spatial and temporal dependencies inherent in event data.We demonstrate the effectiveness of our approach on a dataset of real-world event camera images, where we achieve significant improvements in pose estimation accuracy compared to state-of-the-art methods.Additionally, our method exhibits robustness to varying lighting conditions, motion blur, and sensor noise, highlighting its potential for deployment in a wide range of applications, such as robotics, autonomous vehicles, and augmented reality.Our experimental results showcase the promising capabilities of transformer-based models in leveraging the unique properties of event cameras for accurate and efficient 6-DOF pose estimation.