Adaptive 3D Object Pose Estimation Through Particle Swarm Optimization

  • Akbar AssaEmail author
  • Farrokh Janabi-Sharifi
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 233)


Estimating the 3D pose of objects is an important problem in vision-based robotics. Kalman filters are commonly used as efficient solutions to this problem. However, the performance of these filters deteriorates when system’s noise statistics are not known a priori. This work proposes an adaptive scheme based on particle swarm optimization (PSO) to adjust the measurement noise covariance of the filter. The experimental results confirm the effectiveness of the proposed adaptive solution for Kalman-based pose estimation with uncertain noise statistics.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of TorontoTorontoCanada
  2. 2.Ryerson UniversityTorontoCanada

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