语义感知引导的无人机探索：面向语言条件的三维室内建图
Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping
AI总结：
提出SAGE系统，结合CLIP语义提示与覆盖导向探索，在未知室内环境中优先发现目标物体，同时保持高覆盖率。
摘要：
我们提出语义感知引导探索（SAGE），一个用于未知三维室内环境的开放词汇探索系统，该系统在保持覆盖导向行为的同时，允许语义提示重新优先化前沿选择。基于FALCON体积探索器，SAGE通过四个关键组件集成对比语言-图像预训练（CLIP）：以物体为中心的嵌入存储、将最近观测投影到自由-未知边界的时间缓存、用于高相似度检测的物体前沿，以及统一的语义-几何规划成本。该成本函数限制了语义重新加权的影响，确保前沿被优先化而不牺牲总覆盖率。在基于Matterport3D的仿真中，SAGE在地图-查询对上的物体发现方面优于FALCON和纯语义消融。与Finding Things in the Unknown（FTU）相比，SAGE在九个共享地图-查询对上的探索速度提高了9.0到25.9倍，平均加速13.7倍。此外，SAGE的体积吞吐量显著高于FTU。最后，我们在Modal AI Starling 2四旋翼飞行器上，在两种环境中的五次真实飞行中部署了SAGE，配备机载感知和规划以及离板CLIP推理。比较SAGE和FALCON，我们发现虽然FALCON导致更快的探索和更短的建图轨迹，但SAGE在物体发现方面优于FALCON。
英文摘要：
We present Semantic-Aware Guided Exploration, SAGE, a system for open-vocabulary exploration in unknown 3D indoor environments that preserves coverage-oriented behavior while allowing semantic cues to reprioritize frontier selection. Building on the FALCON volumetric explorer, SAGE integrates Contrastive Language-Image Pre-training (CLIP) via four key components: object-centric embedding storage, a temporal cache that projects recent observations onto the free-unknown boundary, object frontiers for high-similarity detections, and a unified semantic-geometric planning cost. This cost function bounds semantic reweighting influence, ensuring frontiers are prioritized without sacrificing total coverage. In Matterport3D-based simulations, SAGE outperforms FALCON and a semantic-only ablation in object discovery across map-query pairs. Compared to Finding Things in the Unknown (FTU), SAGE completes exploration 9.0 to 25.9 times faster across the nine shared map-query pairs, achieving a mean speedup of 13.7. Furthermore, SAGE achieves substantially higher volumetric throughput than FTU. Finally, we deploy SAGE in five real-world flights in two environments on a Modal AI Starling 2 quadrotor with onboard sensing and planning, and offboard CLIP inference. Comparing SAGE and FALCON, we find that while FALCON results in faster exploration and shorter mapping trajectories, SAGE outperforms FALCON in terms of object discovery.
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。
