Adaptive 3D Object Pose Estimation Through Particle Swarm Optimization
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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