Camera pose estimation using particle filters

In this paper we propose a pose estimation algorithm based on Particle filtering which uses LED sightings gathered from wireless sensor nodes (WSN) to estimate the pose of the camera. The LEDs act as (visual) markers for our pose estimation algorithm. We also compare the performance of our pose estimation algorithm against two reference algorithms - (i) Extended Kalman filtering (EKF) and (ii) Discrete Linear Transform (DLT) based approaches. The performance of all the three algorithms are evaluated for different camera frame rates, varying level of measurement noise and for different marker distribution. Our results (small-scale experimental and room-level simulation studies) show that the particle filtering algorithm gives an accuracy of a few millimetres in position and a few degrees in orientation.