Discovering unknowns: Context-enhanced anomaly detection for curiosity-driven autonomous underwater exploration
作者：
Highlights：
• Anomaly detection for unknowns towards to autonomous underwater exploration.
• Autoencoder and autoregressive network to identify anomalies in unstructured dynamic underwater moving views.
• Novel context-enhanced autoregressive network to learn feature dependence.
• Patch learning paradigm to build an accurate latent feature space.
• Validation on two benchmarks, simulation, and real data, outperforms state-of-arts.
摘要
•Anomaly detection for unknowns towards to autonomous underwater exploration.•Autoencoder and autoregressive network to identify anomalies in unstructured dynamic underwater moving views.•Novel context-enhanced autoregressive network to learn feature dependence.•Patch learning paradigm to build an accurate latent feature space.•Validation on two benchmarks, simulation, and real data, outperforms state-of-arts.
论文关键词：Anomaly detection,Learning unknown objects,Deep learning autoencoder,Autonomous underwater robotics
论文评审过程：Received 24 June 2021, Revised 24 May 2022, Accepted 16 June 2022, Available online 17 June 2022, Version of Record 21 June 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108860
