  Precise camera tracking, high-fidelity 3D tissue reconstruction, and
real-time online visualization are critical for intrabody medical imaging
devices such as endoscopes and capsule robots. However, existing SLAM
(Simultaneous Localization and Mapping) methods often struggle to achieve both
complete high-quality surgical field reconstruction and efficient computation,
restricting their intraoperative applications among endoscopic surgeries. In
this paper, we introduce EndoGSLAM, an efficient SLAM approach for endoscopic
surgeries, which integrates streamlined Gaussian representation and
differentiable rasterization to facilitate over 100 fps rendering speed during
online camera tracking and tissue reconstructing. Extensive experiments show
that EndoGSLAM achieves a better trade-off between intraoperative availability
and reconstruction quality than traditional or neural SLAM approaches, showing
tremendous potential for endoscopic surgeries. The project page is at
https://EndoGSLAM.loping151.com
