用高斯点云实现动态场景中物体的精准定位与自然语言查询。
Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM
- 基于高斯点云的SLAM框架，为每个点分配物体标识符。
- 支持开放词汇自然语言查询，定位准确率超90%。
- 适合机器人导航与交互系统，实时性好、适应复杂环境。
我们提出Go-SLAM，一种新型框架，利用3D高斯点云SLAM重建动态环境，并在场景表示中嵌入物体级信息。该框架采用先进的物体分割技术，为每个高斯点赋予唯一标识符，对应其所代表的物体。由此，系统支持开放词汇查询，用户可通过自然语言描述定位物体。此外，框架包含最优路径生成模块，可在考虑障碍物和环境不确定性的情况下，为机器人计算高效导航路径。在多种场景设置下的综合评估表明，该方法在高质量场景重建、精确物体分割、灵活物体查询及高效机器人路径规划方面均表现优异。本工作进一步推动了三维场景重建、语义物体理解与实时环境交互之间的融合。
原文摘要 · Abstract (English)
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.
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