Authors:Shang-En Tsai (1), Wei-Cheng Sun (1) ((1) Department of Computer Science and Information Engineering, Chang Jung Christian University)

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Abstract:Specular glare on reflective floors, glass boundaries, and glossy indoor surfaces can corrupt active-stereo RGB-D measurements, producing holes and spikes that persist as phantom obstacles in navigation costmaps. This article presents a glare-resilient RGB-D sensor-fusion method based on explicit per-pixel depth reliability. A lightweight Depth Reliability Map network (DRM-Net) predicts sensor trustworthiness, and reliability-guided fusion (RGF) combines continuous weighting with a minimum gate before occupancy integration. Rejected measurements generate neither obstacle insertion nor free-space clearing; affected cells remain unknown or retain prior evidence. Training targets are built from a five-frame, pose-aligned multiview buffer using independent LiDAR/AMCL poses, occlusion-aware aggregation, and a calibrated range-dependent depth-uncertainty model. The evaluation includes tuned nvblox TSDF, Intel RealSense SDK postprocessing, high-threshold stereoconfidence filtering, TDCNet, and HDCNet baselines, together with statistical, safety, generalization, and embedded-runtime analyses. Under severe glare, Depth Reliability Map (DRM)+RGF achieves false obstacle rate (FOR) 0.056 +/- 0.012, free-space recall (FSR) 0.897 +/- 0.045, FNOR 0.018, 1.00 +/- 0.00 degraded-mode safety interventions per 10 m, and 91.4% task success while operating at 16.5 ms per frame. Across the retained reflective-scene trials (Baseline N = 98; DRM+RGF N = 105), collisions decrease from 14 to 1. These results support RGF as a favorable safety-utility tradeoff relative to aggressive filtering and dense completion for glare-affected indoor navigation.

Submission history

From: Shang-En Tsai [view email]
[v1] Tue, 14 Apr 2026 14:00:51 UTC (923 KB)
[v2] Thu, 3 Sep 2026 11:41:38 UTC (1,549 KB)