期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 6, 期 2, 页码 2264-2271出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3061309
关键词
Non-linear optimization; SLAM; scale-space image alignment; visual odometry
类别
The algorithm proposed in the letter improves the robustness of image alignment by optimizing scale parameters and camera pose parameters, adapting to scale changes at different levels. Experiments show that the algorithm outperforms the current state-of-the-art fixed scale pyramid alignment method on the TUM RGB-D dataset.
In this letter, we propose a novel dense 3D image alignment algorithm that estimates the Euclidean transformation between pairs of camera poses from pixel intensities. The novelty consists in the automatic scale adaptation within each level of a multi-resolution image pyramid, using the scale-space representation of images. This is done through the continuous optimization of a scale parameter along with camera pose parameters in the same optimization framework. The proposed approach permits to significantly improve the robustness of the direct image alignment to large inter-frame motion. Various experiments on the TUM RGB-D dataset show that the proposed algorithm outperforms a fixed scale pyramid-based state-of-the-art alignment method.
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