在未知空间中寻找目标：基于微型飞行器（MAV）的以语义对象为中心的探索
Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV
AI总结：
本研究拓展机器人探索范畴，提出融合语义与对象级子地图的MAV探索框架，通过调整目标函数新增距离约束，实现未知空间中特定对象的高效寻找与精准重建，并经仿真与真实场景验证。
摘要：
利用自主移动机器人探索未知空间是一个已被充分研究的问题。在本研究中，我们拓展了探索的范畴，不再局限于尽可能多地探明自由空间这一纯几何目标。我们认为，对于许多实际应用而言，探索应结合环境的语义与对象级理解，以实现面向特定任务的探索。本文研究了在未知空间中寻找特定对象并将其重建至目标细节水平的任务。为此，我们对环境重建进行了扩展，使其不仅包含背景地图，还涵盖对象级且经过语义融合的子地图。重要的是，我们对此前提出的“在尽可能短的时间内探明尽可能多自由空间”的目标函数进行了调整，新增了两个要素：第一，我们对背景表面设置了最大观测距离，以确保目标对象不会因尺寸过小而无法被基于图像的检测器检测到；第二，我们对已发现的对象设置了更小的最大距离，以按所需精度对其进行重建。我们进一步基于Habitat构建了一个微型飞行器（MAV）语义探索模拟器，用于定量验证我们的框架如何作为探索的一部分高效地寻找特定对象。最后，我们展示了该能力可部署于真实场景中，所用无人机搭载了Intel RealSense D455 RGB-D相机。
英文摘要：
Exploration of unknown space with an autonomous mobile robot is a well-studied problem. In this work we broaden the scope of exploration, moving beyond the pure geometric goal of uncovering as much free space as possible. We believe that for many practical applications, exploration should be contextualised with semantic and object-level understanding of the environment for task-specific exploration. Here, we study the task of both finding specific objects in unknown space as well as reconstructing them to a target level of detail. We therefore extend our environment reconstruction to not only consist of a background map, but also object-level and semantically fused submaps. Importantly, we adapt our previous objective function of uncovering as much free space as possible in as little time as possible with two additional elements: first, we require a maximum observation distance of background surfaces to ensure target objects are not missed by image-based detectors because they are too small to be detected. Second, we require an even smaller maximum distance to the found objects in order to reconstruct them with the desired accuracy. We further created a Micro Aerial Vehicle (MAV) semantic exploration simulator based on Habitat in order to quantitatively demonstrate how our framework can be used to efficiently find specific objects as part of exploration. Finally, we showcase this capability can be deployed in real-world scenes involving our drone equipped with an Intel RealSense D455 RGB-D camera.
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。
