Abstract
The target-driven visual navigation is a popular learning-based method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at adapting to unseen environments. In this paper, a navigation method based on Semantic Spatial Relationships (SSR) is proposed and is shown to have more reliable performance when dealing with novel conditions. The construction of joint semantic hierarchical feature vector allows for learning implicit relationship between current observation and target objects, which benefits from construction of prior knowledge graph and semantic space. This differs from the traditional target driven methods, which integrate the visual input vector directly into the reinforcement learning path planning module. Moreover, the proposed method takes both local and global features of observed image into consideration and is thus less conservative and more robust in regards to random scenes. An additional analysis indicates that the proposed SSR performs well on classical metrics. The effectiveness of the proposed SSR model is demonstrated comparing with state-of-the-art methods in unknown scenes.
Supported by National Natural Science Foundation of China \(No. 62073004\), Science and Technology Plan of Shenzhen \(No. JCYJ20190808182209321\).
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Guo, J., Lu, Z., Wang, T., Huang, W., Liu, H. (2021). Object Goal Visual Navigation Using Semantic Spatial Relationships. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_7
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