  We propose SemGauss-SLAM, a dense semantic SLAM system utilizing 3D Gaussian
representation, that enables accurate 3D semantic mapping, robust camera
tracking, and high-quality rendering simultaneously. In this system, we
incorporate semantic feature embedding into 3D Gaussian representation, which
effectively encodes semantic information within the spatial layout of the
environment for precise semantic scene representation. Furthermore, we propose
feature-level loss for updating 3D Gaussian representation, enabling
higher-level guidance for 3D Gaussian optimization. In addition, to reduce
cumulative drift in tracking and improve semantic reconstruction accuracy, we
introduce semantic-informed bundle adjustment leveraging multi-frame semantic
associations for joint optimization of 3D Gaussian representation and camera
poses, leading to low-drift tracking and accurate mapping. Our SemGauss-SLAM
method demonstrates superior performance over existing radiance field-based
SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet
datasets, while also showing excellent capabilities in high-precision semantic
segmentation and dense semantic mapping.
