Visual Simultaneous Localization and Mapping (VSLAM) is a critical technology for autonomous driving and mobile robotics. Traditional VSLAM methods based on discrete representations, such as point clouds, offer high computational efficiency and excellent localization accuracy, but they exhibit limited robustness. In contrast, methods employing field representations, like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D GS), provide greater robustness at the expense of increased computational demands and reduced localization accuracy. Hybrid VSLAM approaches that attempt to combine these representations typically rely on serial, synchronous cascades, which compromise robustness, computational efficiency, and GPU memory usage. This paper introduces a novel adaptive cross-representation VSLAM framework that applies different representation modeling techniques to distinct regions of an image sequence and adopts asynchronous parallel modeling in overlapping regions. A Vision Language Model (VLM) is used to analyze the image sequence, enabling the detection of representation modeling regions and adaptive switching between representations. Cross-representation data association is performed through a coarse-to-fine feature selection process, resulting in a globally consistent map. The proposed method is evaluated on both public and custom-collected datasets, where experimental results show that it surpasses state-of-the-art methods in terms of robustness, computational efficiency, localization accuracy, and GPU memory usage.

Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://github.com/jisehua/Degenerate-Detection.git. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-SLAM systems.