We introduce Go-SLAM, a novel framework that utilizes 3D Gaussian Splatting SLAM to reconstruct dynamic environments while embedding object-level information within the scene representations. This framework employs advanced object segmentation techniques, assigning a unique identifier to each Gaussian splat that corresponds to the object it represents. Consequently, our system facilitates open-vocabulary querying, allowing users to locate objects using natural language descriptions. Furthermore, the framework features an optimal path generation module that calculates efficient navigation paths for robots toward queried objects, considering obstacles and environmental uncertainties. Comprehensive evaluations in various scene settings demonstrate the effectiveness of our approach in delivering high-fidelity scene reconstructions, precise object segmentation, flexible object querying, and efficient robot path planning. This work represents an additional step forward in bridging the gap between 3D scene reconstruction, semantic object understanding, and real-time environment interactions.
我们提出了Go-SLAM，一个利用3D高斯分布SLAM的新框架，用于在重建动态环境的同时将物体级别的信息嵌入场景表示中。该框架采用先进的物体分割技术，为每个与物体对应的高斯点赋予唯一标识符。由此，我们的系统支持开放词汇查询，允许用户通过自然语言描述定位物体。此外，该框架包含一个最优路径生成模块，能够为机器人计算前往查询物体的高效导航路径，同时考虑障碍物和环境不确定性。在多种场景设置中的全面评估表明，我们的方法在高保真场景重建、精确物体分割、灵活物体查询以及高效机器人路径规划方面表现出色。此项工作进一步推动了3D场景重建、语义物体理解与实时环境交互之间的融合。