An Object-Driven Navigation Strategy Based on Active Perception and Semantic Association

Efficiently navigating to a specific kind of objects in an unknown environment is an important and challenging research topic in Embodied AI. Existing methods, such as end-to-end learning ones and modular ones still struggle at this task as they have poor efficiency, interpretability and/or generalization. This letter proposes an object-driven navigation strategy for robots based on active perception and semantic association. Specifically, this strategy enables robots to explore the environment and complete object-driven navigation tasks by endowing them with the ability to explore areas, infer associations, remember scenes, continue navigation, and so on. On this basis, this letter designs a semantic association model based on a graph convolution network using rich information hidden in the environment, including geometric and semantic information, to dynamically predict the association between objects, providing more accurate and effective prior knowledge for object-driven navigation. Experimental results prove that the proposed strategy can help robots better perceive and understand the environment and it is superior to similar strategies in terms of success rate and navigation efficiency.