LDL: Line Distance Functions for Panoramic Localization
Junho Kim Changwoon Choi Hojun Jang Young Min Kim
We introduce LDL, a fast and robust algorithm that localizes a panorama to a
3D map using line segments. LDL focuses on the sparse structural information of
lines in the scene, which is robust to illumination changes and can potentially
enable efficient computation. While previous line-based localization approaches
tend to sacrifice accuracy or computation time, our method effectively observes
the holistic distribution of lines within panoramic images and 3D maps.
Specifically, LDL matches the distribution of lines with 2D and 3D line
distance functions, which are further decomposed along principal directions of
lines to increase the expressiveness. The distance functions provide coarse
pose estimates by comparing the distributional information, where the poses are
further optimized using conventional local feature matching. As our pipeline
solely leverages line geometry and local features, it does not require costly
additional training of line-specific features or correspondence matching.
Nevertheless, our method demonstrates robust performance on challenging
scenarios including object layout changes, illumination shifts, and large-scale
scenes, while exhibiting fast pose search terminating within a matter of
milliseconds. We thus expect our method to serve as a practical solution for
line-based localization, and complement the well-established point-based
paradigm. The code for LDL is available through the following link:
https://github.com/82magnolia/panoramic-localization.