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.

翻译：自主移动机器人对未知空间的探索是一个已被广泛研究的问题。在本工作中，我们拓展了探索的范畴，超越了单纯从几何角度尽可能多地发现自由空间的目标。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，以服务于特定任务的探索。在此，我们研究了在未知空间中寻找特定对象并按照目标细节程度对其进行重建的任务。因此，我们将环境重建扩展为不仅包含背景地图，还包含对象级和语义融合的子地图。重要的是，我们通过增加两个额外元素，改进了先前用于在最短时间内尽可能多地发现自由空间的目标函数：首先，我们要求对背景表面设定最大观测距离，以确保基于图像的检测器不会因目标物体过小无法探测而遗漏它们；其次，我们要求对已发现对象设定更小的最大距离，以便以所需精度重建它们。此外，我们基于Habitat模拟器创建了一个微型飞行器语义探索仿真环境，以定量展示我们的框架如何在探索过程中高效地找到特定对象。最后，我们展示了该能力可在现实场景中部署，涉及搭载Intel RealSense D455 RGB-D相机的无人机。