  We present the first application of 3D Gaussian Splatting in monocular SLAM,
the most fundamental but the hardest setup for Visual SLAM. Our method, which
runs live at 3fps, utilises Gaussians as the only 3D representation, unifying
the required representation for accurate, efficient tracking, mapping, and
high-quality rendering. Designed for challenging monocular settings, our
approach is seamlessly extendable to RGB-D SLAM when an external depth sensor
is available. Several innovations are required to continuously reconstruct 3D
scenes with high fidelity from a live camera. First, to move beyond the
original 3DGS algorithm, which requires accurate poses from an offline
Structure from Motion (SfM) system, we formulate camera tracking for 3DGS using
direct optimisation against the 3D Gaussians, and show that this enables fast
and robust tracking with a wide basin of convergence. Second, by utilising the
explicit nature of the Gaussians, we introduce geometric verification and
regularisation to handle the ambiguities occurring in incremental 3D dense
reconstruction. Finally, we introduce a full SLAM system which not only
achieves state-of-the-art results in novel view synthesis and trajectory
estimation but also reconstruction of tiny and even transparent objects.
