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得到充分研究的问题。本研究拓宽了探索范畴，不再局限于最大化覆盖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物体级理解，以实现特定任务导向的探索。本文聚焦于在未知空间中定位特定物体，并将其重构至目标精度等级的双重任务。为此，我们扩展环境重建框架，不仅构建背景地图，还生成物体级与语义融合的子地图。关键创新在于，我们在先前最大化自由空间覆盖率与最小化探索时间的目标函数中引入两个新约束：第一，设定背景表面最大观测距离，确保基于图像的检测器不会因目标物体过小而导致漏检；第二，针对已发现物体，进一步限定更小的最大观测距离，以实现预期精度的三维重建。基于Habitat模拟器，我们构建了微型无人飞行器（MAV）语义探索仿真平台，定量验证了本框架在探索过程中高效定位特定物体的能力。最后，通过搭载Intel RealSense D455 RGB-D相机的真实无人机系统，展示了该方法在真实场景中的部署可行性。