Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps

• URL: http://arxiv.org/abs/2606.03421v1
• Date: Tue, 02 Jun 2026 10:06:15 GMT
• Title: Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps
• Abstract summary: Specular glare on reflective floors, glass boundaries, and glossy indoor surfaces frequently corrupts active-stereo RGB-D depth measurements.<n>This paper presents a glare-resilient costmap construction method based on explicit depth-reliability modeling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Specular glare on reflective floors, glass boundaries, and glossy indoor surfaces frequently corrupts active-stereo RGB-D depth measurements, producing holes and spikes that accumulate as persistent phantom obstacles in occupancy-grid costmaps. This paper presents a glare-resilient costmap construction method based on explicit depth-reliability modeling. A lightweight Depth Reliability Map network (DRM-Net) predicts per-pixel measurement trustworthiness under specular interference, and a reliability-guided weighted-and-gated fusion (RGF) mechanism modulates occupancy updates before corrupted measurements are accumulated into the map. To support robust training and evaluation, the method uses pose-aligned multi-view reference-depth construction to reduce circular-supervision bias and is evaluated through fusion-variant ablations, parameter-sensitivity analysis, cross-condition tests, paired navigation comparisons, reliability-map metrics, and embedded runtime profiling. Experiments on a real mobile robotic platform equipped with an Intel RealSense D435 and a Jetson Orin Nano show that the proposed method reduces false obstacle insertion, improves free-space preservation, and maintains real-time throughput under reflective-floor, glass-wall, and natural-light glare conditions. These results support treating glare as a measurement-reliability problem rather than as a dense depth-completion problem for safety-critical indoor navigation.

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