A Robust Cardinalized Probability Hypothesis Density Filter for Acoustic SLAM

Heavy-tailed noise in complex environments could seriously degrade the localization performance of acoustic simultaneous localization and mapping (ASLAM). To address this problem, a robust cardinalized probability hypothesis density filter for acoustic SLAM based on variational Bayesian algorithm and unscented Kalman filter (UK-RCPHD ASLAM) is proposed in this article. Specifically, the direction of arrival (DoA) observations and the number of sound sources are modeled as a random finite set (RFS). Then, the bearing-only source states are estimated using the Gaussian mixture (GM) cardinalized probability hypothesis density (CPHD) filter, where the state update with nonlinear observations is completed by the unscented Kalman filter (UKF). Next, the CPHD posterior likelihood is approximated using a variational Bayesian algorithm. Finally, the number and location of sound sources as well as the robot’s trajectory are jointly estimated based on the proposed UK-RCPHD ASLAM. The proposed method has good localization performance and strong robustness against non-Gaussian outliers. Additionally, it does not require a complex data association process and can adapt to changes in the number of sound sources. Experimental results validate the effectiveness of the proposed method.

• Publication year

  2025

• Venue

  IEEE Transactions on Instrumentation and Measurement

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/TIM.2025.3638920
• External record

  Open on Semantic Scholar

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  Semantic Scholar

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• No concepts are published for this paper.

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