期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 44, 期 2, 页码 217-227出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2013.2252339
关键词
3-D object pose estimation; adaptive; extended Kalman filter; iterative; robust estimation; sensor fusion
类别
资金
- Natural Sciences and Engineering Research Council of Canada [DG-903060-07, CRDPJ-350266-07]
Object pose estimation is of great importance to many applications, such as augmented reality, localization and mapping, motion capture, and visual servoing. Although many approaches based on a monocular camera have been proposed, only a few works have concentrated on applying multicamera sensor fusion techniques to pose estimation. Higher accuracy and enhanced robustness toward sensor defects or failures are some of the advantages of these schemes. This paper presents a new Kalman-based sensor fusion approach for pose estimation that offers higher accuracy and precision, and is robust to camera motion and image occlusion, compared to its predecessors. Extensive experiments are conducted to validate the superiority of this fusion method over currently employed vision-based pose estimation algorithms.
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