Abstract
RGB-D simultaneous localization and mapping (SLAM) remains challenging in glass-rich environments with many glass walls and windows because depth measurements in glass regions are often missing, incorrect, or associated with background surfaces. Existing glass-aware methods mainly suppress unreliable observations in glass regions, leaving the glass surfaces themselves unused as landmarks in pose optimization despite their potential to provide additional geometric constraints. Meanwhile, conventional plane-based SLAM methods exploit planar structures to constrain camera motion, but they cannot reliably recover physical glass planes from raw RGB-D measurements because of glass transparency. To bridge this gap, we propose GlassPlane-SLAM, which recovers glass-plane geometry from noisy depth measurements of approximately coplanar surrounding structures and uses the recovered planes as shared landmarks to constrain camera motion across keyframes. A two-stage random sample consensus (RANSAC)-based procedure groups geometrically consistent glass blocks and refits unified planes from their aggregated measurements, reducing the influence of unreliable local observations on the estimated glass geometry. The resulting plane landmarks are jointly optimized with conventional point landmarks and camera poses in local bundle adjustment, while useful point correspondences are preserved. We further construct the UT-SRL Glass RGB-D Dataset to address the lack of RGB-D SLAM data covering long trajectories in glass environments. It contains 38 room-scale and large-scale sequences with pseudo-ground-truth trajectories estimated using FAST-LIVO2. /revBExperiments demonstrate improved localization accuracy on many glass-rich sequences, with particularly pronounced gains in large-scale environments. Qualitative mapping results further demonstrate improved geometric consistency in glass-rich environments.