Visual dense SLAM can facilitate pose estimation and map reconstruction for sensor carriers in unknown environments. However, in uncontrolled environments such as offices, shopping malls, and train stations, frequent occurrences of people walking back and forth or temporary movement of objects within the scene are common. Most existing visual dense SLAM systems do not account for these dynamic factors, leading to localization drift and map distortion. In this paper, we propose DGS-SLAM, a system capable of achieving robust localization and high-fidelity static map reconstruction in dynamic environments. We utilize semantic 3D Gaussians for scene representation, effectively eliminating interference from dynamic objects and refining the reconstruction of static background. We enhance the tracking accuracy and mapping quality of dense SLAM by using a distance distribution-based Gaussian pruning algorithm and implementing a coarse-to-fine tracking strategy with bundle adjustment and differentiable rendering. We perform qualitative and quantitative evaluations on two publicly available dynamic environment datasets. The results indicate that our method effectively reduces the interference caused by dynamic objects, enabling visual dense SLAM to maintain competitive tracking accuracy and mapping performance in dynamic environments.

The success of 3D Gaussian splatting in 3D reconstruction has recently led to efforts to integrate it with SLAM systems. However, most existing research has focused on indoor tracking and mapping, while outdoor Gaussian SLAM methods still heavily rely expensive LiDAR sensor. To address these challenges, we propose LOG-SLAM, a novel method for large-scale outdoor tracking and mapping using Gaussian Splatting. Our approach supports tracking through monocular or visual-inertial input, progressively constructing the 3D Gaussian map from depth and pose estimates obtained during the tracking process. Additionally, we introduce a submap-based strategy for managing large-scale maps, enabling the reconstruction of kilometer-scale environments. A loop closure detection module is also incorporated to reduce accumulated errors. Furthermore, we present a novel dynamic object removal method based on rendering loss that mitigates the interference of dynamic objects on the reconstruction. Our experiments on KITTI and KITTI-360 demonstrate that our method achieves localization performance comparable to traditional SLAM systems, while outperforming recent GS/NeRF-based SLAM approaches in terms of mapping and rendering quality.

Neural implicit representation has recently achieved significant advancements, especially in the field of SLAM(Simultaneous Localization and Mapping). Previous NeRF-based SLAM methods have difficulties with object-level localization and reconstruction and struggle in dynamic and illumination-varied environments. We propose ONeK-SLAM, a robust object-level SLAM system that effectively combines feature points and neural radiance fields. ONeK-SLAM uses the joint information at the object level to improve localization accuracy and enhance reconstruction details. Moreover, our approach detects and eliminates dynamic objects based on the joint errors, while also harnessing the illumination invariance offered by feature points. Consequently, ONeK-SLAM achieves high-precision localization and detailed object-level mapping, even in dynamic and illumination-varying environments. Our evaluations, conducted on three public datasets that include both dynamic and variable lighting sequences, demonstrate that our method outperforms recent NeRF-based SLAM method in both localization and reconstruction.

In large-scale outdoor environments, vehicles often encounter situations like retracing their path or turning around, leading to many reverse loop closures where the vehicles traverse previously covered paths from opposite viewpoints. Existing monocular SLAM methods, due to insufficient utilization of semantic information and neglect of leveraging reverse loop closures, result in significant scale drift and pose drift when confronted with such scenarios. In this paper, we introduce SMORE-SLAM, a semantic monocular SLAM with scale correction and reverse loop closure module. We constrain scale drift by harnessing semantic information across a wide spatial extent. Furthermore, we detect and correct reverse loop closures using semantic point cloud to reduce pose drift. Experimental results on the KITTI odometry dataset and the Oxford RobotCar dataset demonstrate the capability of our research in scale correction and reverse loop closure detection, enabling a reduction in trajectory errors of monocular SLAM.