标题： VISTA：开放词汇、任务相关的机器人探索与在线语义高斯点云 显示英文标题

标题： VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting

摘要： 我们提出VISTA（基于视角的图像选择与语义任务感知），这是一种用于机器人规划信息轨迹以提高任务完成相关区域的3D地图质量的主动探索方法。 给定一个开放词汇搜索指令（例如，“找到一个人”），VISTA使机器人能够探索其环境以寻找感兴趣的物体，同时构建场景的实时语义3D高斯点云重建。 机器人通过规划分阶段的轨迹来导航其环境，这些轨迹优先考虑与查询的语义相似性以及对环境未探索区域的探索。 为了评估轨迹，VISTA引入了一种新颖且高效的视角-语义覆盖度量标准，该标准量化了3D场景中的几何视图多样性和任务相关性。 在静态数据集上，我们的覆盖度量标准在计算速度和重建质量方面优于最先进的基线方法FisherRF和Bayes' Rays。 在四旋翼硬件实验中，与基线方法相比，VISTA在具有挑战性的地图中实现了6倍更高的成功率，而在较不具挑战性的地图中则保持了基线性能。 最后，我们通过在四旋翼无人机和Spot四足机器人上部署VISTA证明了其平台无关性。 论文被接受后将发布开源代码。 显示英文摘要

摘要： We present VISTA (Viewpoint-based Image selection with Semantic Task Awareness), an active exploration method for robots to plan informative trajectories that improve 3D map quality in areas most relevant for task completion. Given an open-vocabulary search instruction (e.g., "find a person"), VISTA enables a robot to explore its environment to search for the object of interest, while simultaneously building a real-time semantic 3D Gaussian Splatting reconstruction of the scene. The robot navigates its environment by planning receding-horizon trajectories that prioritize semantic similarity to the query and exploration of unseen regions of the environment. To evaluate trajectories, VISTA introduces a novel, efficient viewpoint-semantic coverage metric that quantifies both the geometric view diversity and task relevance in the 3D scene. On static datasets, our coverage metric outperforms state-of-the-art baselines, FisherRF and Bayes' Rays, in computation speed and reconstruction quality. In quadrotor hardware experiments, VISTA achieves 6x higher success rates in challenging maps, compared to baseline methods, while matching baseline performance in less challenging maps. Lastly, we show that VISTA is platform-agnostic by deploying it on a quadrotor drone and a Spot quadruped robot. Open-source code will be released upon acceptance of the paper.