LDL: Line Distance Functions for Panoramic Localization
Junho Kim, Changwoon Choi, Hojun Jang, Young Min Kim

TL;DR
LDL is a fast, robust panoramic localization algorithm that uses line segment distributions for accurate pose estimation in challenging scenarios, without requiring training or costly correspondence matching.
Contribution
Introduces LDL, a novel line-based localization method that efficiently matches line distributions for pose estimation in panoramic images.
Findings
Robust performance under illumination changes and scene variations
Fast pose estimation within milliseconds
Effective in large-scale and challenging environments
Abstract
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…
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Code & Models
Videos
LDL: Line Distance Functions for Panoramic Localization· youtube
Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Advanced Vision and Imaging
