2021
DOI: 10.1016/j.eswa.2020.113755
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Improving Bayesian inference efficiency for sensory anomaly detection and recovery in mobile robots

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Cited by 12 publications
(7 citation statements)
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“…Unfolding cycles up to a bounded depth has been applied in the setting of a robotic sensor system by [2]. In their use case, only cycles of length two may appear, and only the nodes appearing on the cycles are implicitly used as cutset for the unfolding.…”
Section: Related Workmentioning
confidence: 99%
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“…Unfolding cycles up to a bounded depth has been applied in the setting of a robotic sensor system by [2]. In their use case, only cycles of length two may appear, and only the nodes appearing on the cycles are implicitly used as cutset for the unfolding.…”
Section: Related Workmentioning
confidence: 99%
“…In [25], an equilibrium semantics is sketched that is similar to our Markov chain semantics, albeit based on variable oderings rather than cutsets. Determining independence relations, Markov properties, and joint distributions are central problems addressed for cyclic causal BNs [2,5,20,24,29]. Markov properties and joint distributions for extended versions of causal BNs have been considered recently, e.g., in directed graphs with hyperedges (HEDGes) [5] and cyclic structural causal models (SCMs) [2].…”
Section: Related Workmentioning
confidence: 99%
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“…It is applied as a tool that helps in the maintenance of robotics systems, by dealing with multivariate time series problems. A more recent research [21] proposes the application of Bayesian networks. As they allow to interrelate different heterogeneous data sources, are applied to perceive anomalies in mobile robots and to recover from them.…”
Section: Related Workmentioning
confidence: 99%
“…In [ 20 ], a linear Kalman filter was implemented in an algorithm of 3D orientation detection by combining data from three sensors (a tri-axis accelerometer, a tri-axis gyroscope, and a tri-axis magnetometer). Other examples of sensor fusion applications are reported in [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], where sensor data fusion was implemented using neural networks or Bayesian inference. However, such an approach of combining data from different sensors has never been adopted so far for inductive magnetic measurements.…”
Section: Introductionmentioning
confidence: 99%