Degenerancy-Resilient LIDAR Odometry via Reflectance-Dervied Correspondences

Simple item page
dc.contributor.authorMarmaglio, Simoneen_US
dc.contributor.authorNguyen Hoang, Namen_US
dc.contributor.authorSavardi, Mattiaen_US
dc.contributor.authorSgrenzaroli, Matteoen_US
dc.contributor.authorVassena, Giorgioen_US
dc.contributor.authorSignoroni, Albertoen_US
dc.contributor.editorComino Trinidad, Marcen_US
dc.contributor.editorMancinelli, Claudioen_US
dc.contributor.editorMaggioli, Filippoen_US
dc.contributor.editorRomanengo, Chiaraen_US
dc.contributor.editorCabiddu, Danielaen_US
dc.contributor.editorGiorgi, Danielaen_US
dc.date.accessioned2025-11-21T07:28:23Z
dc.date.available2025-11-21T07:28:23Z
dc.date.issued2025
dc.description.abstractThe key challenge in LiDAR odometry is estimating motion in geometrically degenerate environments, where standard geometrybased feature alignment often fails. LiDAR reflectance offers complementary information: it can be rendered as an image, letting the LiDAR to act like an active camera sensor and to add constraints where geometry is weak. We use these images to detect repeatable keypoints, match them across sweeps, and lift their locations to 3D, creating sparse and reflectance-informed correspondences. Our method follows a standard LiDAR-Inertial Odometry (LIO) pipeline. An Error-State Kalman Filter (ESKF) provides high-rate motion estimates for scan deskewing and for initializing ICP. We fuse reflectance-derived constraints into scan-to-map registration with a joint objective that combines a sparse point-to-point term with point-to-plane residuals, stabilizing motion directions that are otherwise weakly observable. We also select both reflectance and geometric correspondences to specifically constrain these weak directions. Experiments in geometrically degenerate and GNSS-denied settings, and even in presence of highly spatially anisotropic LiDAR acquisitions, show that adding reflectance-derived correspondences reduces drift and guides convergence toward the true pose.en_US
dc.description.sectionheadersRobotics and Mixed Reality
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20251332
dc.identifier.isbn978-3-03868-296-7
dc.identifier.issn2617-4855
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20251332
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20251332
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDegenerancy-Resilient LIDAR Odometry via Reflectance-Dervied Correspondencesen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
stag20251332.pdf
Size:
8.45 MB
Format:
Adobe Portable Document Format
Download
Collections
Italian Chapter Conference 2025 - Smart Tools and Apps in Graphics