Zhang Chi, Yang Zhong, Xue Bayang, Zhuo Haoze, Liao Luwei, Yang Xin, Zhu Zekun
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Biomimetics (Basel). 2023 Sep 19;8(5):436. doi: 10.3390/biomimetics8050436.
Geometric-semantic scene understanding is a spatial intelligence capability that is essential for robots to perceive and navigate the world. However, understanding a natural scene remains challenging for robots because of restricted sensors and time-varying situations. In contrast, humans and animals are able to form a complex neuromorphic concept of the scene they move in. This neuromorphic concept captures geometric and semantic aspects of the scenario and reconstructs the scene at multiple levels of abstraction. This article seeks to reduce the gap between robot and animal perception by proposing an ingenious scene-understanding approach that seamlessly captures geometric and semantic aspects in an unexplored environment. We proposed two types of biologically inspired environment perception methods, i.e., a set of elaborate biomimetic sensors and a brain-inspired parsing algorithm related to scene understanding, that enable robots to perceive their surroundings like bats. Our evaluations show that the proposed scene-understanding system achieves competitive performance in image semantic segmentation and volumetric-semantic scene reconstruction. Moreover, to verify the practicability of our proposed scene-understanding method, we also conducted real-world geometric-semantic scene reconstruction in an indoor environment with our self-developed drone.
几何语义场景理解是一种空间智能能力，对于机器人感知和在世界中导航至关重要。然而，由于传感器受限和情况随时间变化，理解自然场景对机器人来说仍然具有挑战性。相比之下，人类和动物能够形成它们所处场景的复杂神经形态概念。这种神经形态概念捕捉场景的几何和语义方面，并在多个抽象层次上重建场景。本文旨在通过提出一种巧妙的场景理解方法来缩小机器人与动物感知之间的差距，该方法能在未探索的环境中无缝捕捉几何和语义方面。我们提出了两种受生物启发的环境感知方法，即一组精心设计的仿生传感器和一种与场景理解相关的受大脑启发的解析算法，使机器人能够像蝙蝠一样感知周围环境。我们的评估表明，所提出的场景理解系统在图像语义分割和体语义场景重建方面取得了有竞争力的性能。此外，为了验证我们提出的场景理解方法的实用性，我们还使用自行研发的无人机在室内环境中进行了真实世界的几何语义场景重建。
