SD-6DoF-ICLK: Sparse and Deep Inverse Compositional Lucas-Kanade Algorithm on SE(3)
This work addresses robust image alignment under severe conditions for applications like odometry and mapping, though it appears incremental as it builds on existing ICLK frameworks.
The paper tackles the problem of robust image alignment for visual-inertial odometry or SLAM by introducing SD-6DoF-ICLK, a learning-based pipeline that uses sparse depth information to optimize relative pose on SE(3), achieving a runtime of 145 ms per image pair and vastly outperforming classical methods.
This paper introduces SD-6DoF-ICLK, a learning-based Inverse Compositional Lucas-Kanade (ICLK) pipeline that uses sparse depth information to optimize the relative pose that best aligns two images on SE(3). To compute this six Degrees-of-Freedom (DoF) relative transformation, the proposed formulation requires only sparse depth information in one of the images, which is often the only available depth source in visual-inertial odometry or Simultaneous Localization and Mapping (SLAM) pipelines. In an optional subsequent step, the framework further refines feature locations and the relative pose using individual feature alignment and bundle adjustment for pose and structure re-alignment. The resulting sparse point correspondences with subpixel-accuracy and refined relative pose can be used for depth map generation, or the image alignment module can be embedded in an odometry or mapping framework. Experiments with rendered imagery show that the forward SD-6DoF-ICLK runs at 145 ms per image pair with a resolution of 752 x 480 pixels each, and vastly outperforms the classical, sparse 6DoF-ICLK algorithm, making it the ideal framework for robust image alignment under severe conditions.