Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV
arxiv(2023)
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.
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