让机器人主动选最佳视角，边探索边理解场景语义。
Understanding while Exploring: Semantics-driven Active Mapping
- 用语义与几何不确定性指导探索决策。
- 在Replica和Matterport3D上提升地图完整度与准确性。
- 适合需要智能导航的机器人系统研发者。
未知环境中的有效机器人自主性依赖于主动探索与对几何及语义的精确理解。本文提出ActiveSGM框架，可在执行前预测潜在观测的有用性。基于3D高斯点阵（3DGS）映射主干，该方法结合语义与几何不确定性量化，以及稀疏语义表示，引导探索过程。通过策略性选择最优观测视角，ActiveSGM显著提升地图完整性、准确性和对噪声语义数据的鲁棒性，从而支持更自适应的场景探索。在Replica与Matterport3D数据集上的实验验证了其在主动语义映射任务中的有效性。
原文摘要 · Abstract (English)
Effective robotic autonomy in unknown environments demands proactive exploration and precise understanding of both geometry and semantics. In this paper, we propose ActiveSGM, an active semantic mapping framework designed to predict the informativeness of potential observations before execution. Built upon a 3D Gaussian Splatting (3DGS) mapping backbone, our approach employs semantic and geometric uncertainty quantification, coupled with a sparse semantic representation, to guide exploration. By enabling robots to strategically select the most beneficial viewpoints, ActiveSGM efficiently enhances mapping completeness, accuracy, and robustness to noisy semantic data, ultimately supporting more adaptive scene exploration. Our experiments on the Replica and Matterport3D datasets highlight the effectiveness of ActiveSGM in active semantic mapping tasks.
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