WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments
Authors: name: Jianhao Zheng, name: Zihan Zhu, name: Valentin Bieri, name: Marc Pollefeys, name: Songyou Peng, name: Iro Armeni

We present WildGS-SLAM, a robust and efficient monocular RGB SLAM system
designed to handle dynamic environments by leveraging uncertainty-aware
geometric mapping. Unlike traditional SLAM systems, which assume static scenes,
our approach integrates depth and uncertainty information to enhance tracking,
mapping, and rendering performance in the presence of moving objects. We
introduce an uncertainty map, predicted by a shallow multi-layer perceptron and
DINOv2 features, to guide dynamic object removal during both tracking and
mapping. This uncertainty map enhances dense bundle adjustment and Gaussian map
optimization, improving reconstruction accuracy. Our system is evaluated on
multiple datasets and demonstrates artifact-free view synthesis. Results
showcase WildGS-SLAM's superior performance in dynamic environments compared to
state-of-the-art methods.