Go-SLAM：基于高斯泼溅SLAM的接地物体分割与定位
Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM
AI总结：
提出Go-SLAM框架，利用3D Gaussian Splatting SLAM重建动态环境并嵌入物体级信息，支持开放词汇查询和最优机器人路径规划，实现高保真重建、精确分割与高效导航。
摘要：
我们提出了Go-SLAM，这是一个利用3D Gaussian Splatting SLAM重建动态环境，同时将物体级信息嵌入场景表示的新颖框架。该框架采用先进的物体分割技术，为每个Gaussian splat分配与其所代表物体相对应的唯一标识符。因此，我们的系统支持开放词汇查询，允许用户使用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，在考虑障碍物和环境不确定性的情况下，为机器人计算通向被查询物体的高效导航路径。在各种场景设置下的综合评估证明了我们方法的有效性，能够提供高保真场景重建、精确的物体分割、灵活的物体查询和高效的机器人路径规划。这项工作在弥合3D场景重建、语义物体理解和实时环境交互之间的差距方面迈出了新的一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
