Improving Bayesian inference efficiency for sensory anomaly detection and recovery in mobile robots
作者：
Highlights：
• A novel Bayesian network framework for modeling sensors in mobile robots.
• Inference algorithm that leverages that model for better efficiency and performance.
• Representation and inference with cyclic dependencies among different sensors.
• Intelligent integration of diverse sources of knowledge.
• Sensory anomaly detection and data rebuilding in real robotic scenarios.
摘要
•A novel Bayesian network framework for modeling sensors in mobile robots.•Inference algorithm that leverages that model for better efficiency and performance.•Representation and inference with cyclic dependencies among different sensors.•Intelligent integration of diverse sources of knowledge.•Sensory anomaly detection and data rebuilding in real robotic scenarios.
论文关键词：Mobile robots,Sensory systems,Bayesian networks inference
论文评审过程：Received 10 February 2020, Revised 8 July 2020, Accepted 12 July 2020, Available online 30 July 2020, Version of Record 5 August 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113755
