  Simultaneous localization and mapping is essential for position tracking and
scene understanding. 3D Gaussian-based map representations enable
photorealistic reconstruction and real-time rendering of scenes using multiple
posed cameras. We show for the first time that using 3D Gaussians for map
representation with unposed camera images and inertial measurements can enable
accurate SLAM. Our method, MM3DGS, addresses the limitations of prior neural
radiance field-based representations by enabling faster rendering, scale
awareness, and improved trajectory tracking. Our framework enables
keyframe-based mapping and tracking utilizing loss functions that incorporate
relative pose transformations from pre-integrated inertial measurements, depth
estimates, and measures of photometric rendering quality. We also release a
multi-modal dataset, UT-MM, collected from a mobile robot equipped with a
camera and an inertial measurement unit. Experimental evaluation on several
scenes from the dataset shows that MM3DGS achieves 3x improvement in tracking
and 5% improvement in photometric rendering quality compared to the current
3DGS SLAM state-of-the-art, while allowing real-time rendering of a
high-resolution dense 3D map. Project Webpage:
https://vita-group.github.io/MM3DGS-SLAM
