EndoGSLAM is an advanced SLAM (Simultaneous Localization and Mapping) framework designed for endoscopic surgeries, aiming to achieve precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization. The method integrates streamlined Gaussian representation and differentiable rasterization to facilitate over 100 fps rendering speed during online camera tracking and tissue reconstruction. Unlike traditional or neural SLAM approaches, EndoGSLAM uses dense photometric loss for real-time tracking and reconstruction, making it robust in complex surgical fields. The system iteratively expands 3D Gaussians on unobserved regions and partially refines the reconstructed surgical field, significantly reducing computational costs. Extensive experiments on the Colonoscopy 3D Video Dataset (C3VD) demonstrate that EndoGSLAM outperforms traditional and neural SLAM methods in terms of optimization speed, rendering quality, and overall system efficiency, showing its potential for advanced surgical navigation. The project page is available at <https://EndoGSLAM.oping151.com>.EndoGSLAM is an advanced SLAM (Simultaneous Localization and Mapping) framework designed for endoscopic surgeries, aiming to achieve precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization. The method integrates streamlined Gaussian representation and differentiable rasterization to facilitate over 100 fps rendering speed during online camera tracking and tissue reconstruction. Unlike traditional or neural SLAM approaches, EndoGSLAM uses dense photometric loss for real-time tracking and reconstruction, making it robust in complex surgical fields. The system iteratively expands 3D Gaussians on unobserved regions and partially refines the reconstructed surgical field, significantly reducing computational costs. Extensive experiments on the Colonoscopy 3D Video Dataset (C3VD) demonstrate that EndoGSLAM outperforms traditional and neural SLAM methods in terms of optimization speed, rendering quality, and overall system efficiency, showing its potential for advanced surgical navigation. The project page is available at <https://EndoGSLAM.oping151.com>.