ED-SLAM: Event-Depth Gaussian Splatting SLAM

Abstract

Event-based Gaussian splatting (GS) reconstruction approach has recently attracted considerable attention. Existing methods usually assume the camera poses are known as a prior, or struggle to process long event streams due to the robustness of the method while poses are not known. In this work, we present ED-SLAM, an Event-Depth Gaussian Splatting-based simultaneous localization and mapping(SLAM) pipeline, which is robust to long event streams and does not require ground-truth camera poses. The pipeline achieves high-accuracy pose estimation and high-fidelity 3D reconstruction thanks to the impressive 3D representation capability of Gaussian splatting. In particular, we propose a novel patch-based event-depth tracking algorithm and seamlessly integrate it into the Gaussian splatting mapping pipeline. Extensive experiments on both synthetic and real-world datasets demonstrate that our method significantly improves tracking accuracy and robustness, and also delivers improved reconstruction performance.