@Article{ahmine:ral:2021,
  author    = {Ahmine, Yassine and Caron, Guillaume and Chouireb, Fatima and Mouaddib, El Mustapha},
  title     = {Continuous Scale-Space Direct Image Alignment for Visual Odometry from RGB-D Images},
  journal   = {IEEE Robotics and Automation Letters},
  year      = {2021},
  volume    = {6},
  number    = {2},
  pages     = {2264--2271},
  month     = {April},
  doi       = {10.1109/LRA.2021.3061309},
  url       = {https://hal.archives-ouvertes.fr/hal-03130945/document},
  keywords  = {Visual odometry, SLAM, Scale-space image alignment, Non-linear optimization, Direct alignment},
  abstract  = {In this paper, 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.},
  publisher = {IEEE-INST Electrical Electronics Engineers Inc},
  address   = {445 Hoes Lane, Piscataway, NJ 08855-4141, USA}
}