Sample-based adaptive Kalman filtering for accurate camera pose tracking

Abstract

Inferring the camera pose with high accuracy is deemed crucial in many applications such as robot manipulation and virtual reality. Traditionally, features extracted from camera images are processed by Kalman-based schemes due to their high efficacy and fast response. Yet, the performance of the filter deteriorates if the parameters of the filter are uncertain. Estimating the noise parameters through conventional adaptive schemes alleviates this problem, yet the estimation’s accuracy still suffers from rough parameter estimations. To improve the accuracy of pose estimations further, this work proposes a novel adaptive scheme which employs a multi-model approach to approximate the system posteriori via sampling the noise and initial state covariance priories and progressively adjusting the filter parameters using the drawn samples. The experimental results confirm the enhanced performance of the proposed adaptive method compared to previously applied adaptive and non-adaptive pose estimation schemes, at the expense of additional complexity.