GlassGuard: Verified Glass Plane Mapping for Robot Navigation

Hanwen Guo; Zhengzhi Lin; Yusen Xie; Ji Zhang

cs.ROOct 1, 2026

GlassGuard: Verified Glass Plane Mapping for Robot Navigation

Authors: Hanwen Guo, Zhengzhi Lin, Yusen Xie, Ji Zhang

Organizations: Carnegie Mellon University, Pittsburgh, PA, USA

Abstract

Transparent and specular surfaces pose a serious challenge to LiDAR-based SLAM and navigation because laser returns may pass through glass, leaving collision boundaries absent from the map. Prior work attempts to reconstruct the missing surfaces, but inaccurate obstacle placement can create the opposite failure: contamination of traversable free space. Recognizing this dual requirement, we present GlassGuard, a navigation-oriented framework for reconstructing planar architectural glass from complementary visual and LiDAR evidence. We formulate success in terms of both glass coverage and free-space contamination and apply this principle throughout proposal verification and global map construction. A foundation vision model provides glass-instance masks, structural 3D cues generate metric plane hypotheses, and depth-free 2D projective geometry checks their orientations before they enter a consolidated global map. We evaluate GlassGuard in nine building-scale scenes spanning diverse glass structures, spatial scales, and lighting conditions, with more than one hour and 2.1 km of real-world robot traversal. GlassGuard achieves 85% of total glass coverage for its panoramic version. Under identical pinhole inputs, GlassGuard achieves 82% total coverage, compared with at most 61% for the evaluated baselines, while producing 5-17x fewer false voxels per frame. Qualitative examples with a navigation planner illustrate the reconstructed planes blocking paths through glass while leaving traversable routes open. The project page is available at https://glassguardproject.github.io/.

Figures & tables

Fig. 1: Top-down candidate-plane failures: (a) slanted placement from a false edge, (b) incorrect depth, and (c) reflection-induced slant. Shading denotes the false blockage : navigable free space blocked by the wrong plane.

Fig. 2: System overview; A–D mark four glass instances traced end to end. 1. Detect (left): Slim SAM3 Detection yields instance masks on the RGB panorama, each cropped to its Mask Frame ; Associate Masks with 3D projects the LiDAR cloud through that frame to obtain seeds, from which vertical and horizontal pillars and bounded plane candidates are built (Sec. IV-C ). 2. Verify Planes (center): Single-Frame Ray-Cast Verification checks each candidate against the depth-free 2D inferred orientation (Sec. IV-D ; crosses rejected, check accepted), and Multi-View Reprojection Verification removes a plane whose off-mask fraction grows as the robot moves. 3. Global Management (right): the Global Seed-Floor Evidence Map drives Floor Eviction , and Global Plane Merge and Absorb resolves overlapping hypotheses by seed count (Sec. IV-E ); surviving planes enter the navigation map.

	GDD			GSD-S test
Method	IoU	Prec.	Rec.	IoU	Prec.	Rec.
Grounded-SAM2 [ 30 ]	0.642	0.781	0.783	0.509	0.563	0.841
YOLO-World [ 31 ]	0.436	0.677	0.550	0.230	0.589	0.274
SAM3 (original)	0.815	0.901	0.895	0.713	0.764	0.915
Slim-2816 (ours)	0.748	0.888	0.826	0.671	0.714	0.918

TABLE I: Glass instance segmentation on GDD and GSD-S at

512^{2}

Fig. 3: Depth-free 2D $\to$ 3D verification. (a) A reliable silhouette edge anchors a support quadrilateral enclosing the observed glass mask; its boundary samples are back-projected as camera rays. (b) Opposite-side interpretation planes recover the pane’s in-plane directions and hence a depth-free reference orientation. (c) The resulting yaw reference is compared with the LiDAR-derived candidate; angular disagreement produces a false blockage that widens along the plane, motivating a length-dependent acceptance threshold.

Method	Camera	LiDAR	Persist. map	VRAM	s/frame
MonoGlass3D	pinhole RGB	—	—	2.4 GB	0.13
GlassRecon	pinhole RGB	✓	—	0.8 GB	3.6
GG-pin (ours)	pinhole RGB	✓	✓	1.7 GB	0.74
GG-360 (ours)	360 ∘ RGB	✓	✓	1.7 GB	0.73

TABLE II: Inputs, peak VRAM, and mean per-frame inference time of the evaluated methods on an RTX 5070 Ti laptop GPU.

Coverage

\uparrow

Spill

\downarrow

Method by 10 m by 8 m by 5 m Ever Air Ground per frame med. dist MonoGlass3D 11.8 18.6 34.6 61.0 1564 490 290.2 4.2 m GlassRecon 16.6 21.0 28.0 44.4 408 150 85.4 3.4 m GG-pin (ours) 32.6 54.2 68.8 82.1 164 94 16.7 3.2 m GG-360 (ours) 47.6 58.0 71.4 85.2 171 55 15.3 3.0 m

0.2 m 0.5 m 2.0 m Ever sp/f Ever sp/f Ever sp/f 22.2 10452 43.4 1461 80.1 52 14.4 2370 30.1 366 58.9 19 52.0 562 72.5 76 90.3 6 60.1 700 79.5 83 89.5 4

TABLE III: Left: nine-scene occupancy comparison at 1.0 m resolution (coverage in percent; spill = distinct air/ground false voxels accumulated over the run, mean concurrent spill per frame, median distance to GT glass). Right: metric sensitivity—Ever coverage / spill per frame at three further voxel resolutions (tolerance scales with the voxel); a GlassGuard variant leads every metric at every resolution.

	$1.0$ m			$0.5$ m
Method	0–2 m	2–5 m	Ret.	0–2 m	2–5 m	Ret.	Spill/frame $\downarrow$
MonoGlass3D	98.1	95.5	— †	84.2	81.5	— †	290.2
GlassRecon	83.1	73.4	— †	63.9	58.4	— †	85.4
GG-pin (ours)	84.4	89.0	91.7	70.1	81.0	86.8	16.7
GG-360 (ours)	89.1	91.5	93.2	81.3	83.8	87.8	15.3

TABLE IV: Nine-scene current-map protection (pooled over demanded voxel-frames) at

1.0

m and

0.5

m voxel resolution, and concurrent spill at

1.0

m. † Baselines are evaluated on their accumulated detections, which are never removed; their retention is 100% by construction and therefore not comparable.

Fig. 4: Glass mapping and local planning across indoor, outdoor, daytime, and nighttime scenes. Columns: RGB, Glass Blind, GG-360, GG-pin, MonoGlass3D, and GlassRecon; azure denotes the local planned path, cyan the global path, and medium blue the predicted glass. Without glass detection, planned paths cross physical glass boundaries in rows 3 and 6, potentially directing the robot toward a collision. Both GG variants recover the glass barriers while largely preserving traversable space. MonoGlass3D and GlassRecon exhibit substantial false occupancy, particularly in rows 1, 4, 5, and 6, and leave gaps in glass barriers in some scenes; under severe map contamination the displayed local paths disappear or become substantially shorter.

Config.	Ever $\uparrow$	Cur.@0–2 m $\uparrow$	Retention $\uparrow$	Spill/fr. $\downarrow$	Acc. spill $\downarrow$
Full GlassGuard	85.2 $\pm$ 4.6	91.7 $\pm$ 7.6	92.2 $\pm$ 4.1	19.4 $\pm$ 7.9	269 $\pm$ 83
– angle gate	86.1 $\pm$ 4.8	97.0 $\pm$ 4.9	93.2 $\pm$ 3.5	34.0 $\pm$ 5.2	607 $\pm$ 377
– multi-view	82.0 $\pm$ 8.4	97.1 $\pm$ 5.5	93.0 $\pm$ 4.0	18.7 $\pm$ 5.3	300 $\pm$ 102
– floor evidence	82.2 $\pm$ 8.0	97.1 $\pm$ 5.9	96.2 $\pm$ 0.5	25.8 $\pm$ 3.5	280 $\pm$ 115
– plane merging	84.4 $\pm$ 9.5	96.7 $\pm$ 4.6	96.8 $\pm$ 1.8 †	33.8 $\pm$ 7.7	371 $\pm$ 116
– global mgmt.	88.2 $\pm$ 6.9	99.0 $\pm$ 2.1 †	99.4 $\pm$ 0.4 †	87.7 $\pm$ 27.2	478 $\pm$ 164

TABLE V: Component ablation on four scenes, GG-pin. Each row removes one component from the full system; the two baselines are evaluated on the same four scenes. Entries are mean

\pm

standard deviation over the four scenes (

n{=}4

; the deviation indicates scene-to-scene dispersion, not a confidence interval). † Retention approaches 100% by construction when little or nothing is ever removed, as for the accumulating baselines of Table IV .

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Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation

Jiamin Zheng, Jingwen Yu, Guangcheng Chen, Hong Zhang

Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, Shenzhen, China · CKS Robotics Institute, Hong Kong University of Science and Technology, Hong Kong SAR, China

Depth Estimation Robotic Perception Robot Navigation Geometric Priors Robot Systems Navigation+1

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Indoor robot navigation is often compromised by glass surfaces, which severely corrupt depth sensor measurements. While foundation models like Depth Anything 3 provide excellent geometric priors, they lack an absolute metric scale. We propose a training-free framework that leverages depth foundation models as a structural prior, employing a robust local RANSAC-based alignment to fuse it with raw sensor depth. This naturally avoids contamination from erroneous glass measurements and recovers an accurate metric scale. Furthermore, we introduce \ti{GlassRecon}, a novel RGB-D dataset with geometrically derived ground truth for glass regions. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art baselines, especially under severe sensor depth corruption. The dataset and related code will be released at https://github.com/jarvisyjw/GlassRecon.

Abstract

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