Open AccessEKFPnP: extended Kalman filter for camera pose estimation in a sequence of images
Author(s) -
Mehralian Mohammad Amin,
Soryani Mohsen
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0606
Subject(s) - artificial intelligence , reprojection error , computer vision , computer science , robustness (evolution) , extended kalman filter , kalman filter , pose , outlier , camera auto calibration , 3d pose estimation , camera resectioning , pattern recognition (psychology) , image (mathematics) , biochemistry , chemistry , gene
In real‐world applications the perspective‐n‐point (PnP) problem should generally be applied to a sequence of images which a set of drift‐prone features are tracked over time. In this study, the authors consider both the temporal dependency of camera poses and the uncertainty of features for the vision‐only sequential camera pose estimation. Using the extended Kalman filter (EKF), a priori estimate of the camera pose is calculated from the camera motion model and then it is corrected by minimising the reprojection error of the reference points. Applying probabilistic approach also provides the covariance of the pose parameters which helps to measure the reliability of the estimated parameters. Experimental results, using both synthetic and real data, demonstrate that the proposed method improves the robustness of the camera pose estimation, in the presence of tracking error and feature matching outliers, compared to the state of the art.