Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps

• URL: http://arxiv.org/abs/2604.12753v1
• Date: Tue, 14 Apr 2026 14:00:51 GMT
• Title: Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps
• Abstract summary: Specular glare on reflective floors and glass surfaces frequently corrupts RGB-D depth measurements.<n>This paper proposes a glare-resilient costmap construction method based on explicit depth-reliability modeling.
• Score: 0.6445605125467574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Specular glare on reflective floors and glass surfaces frequently corrupts RGB-D depth measurements, producing holes and spikes that accumulate as persistent phantom obstacles in occupancy-grid costmaps. This paper proposes a glare-resilient costmap construction method based on explicit depth-reliability modeling. A lightweight Depth Reliability Map (DRM) estimator predicts per-pixel measurement trustworthiness under specular interference, and a Reliability-Guided Fusion (RGF) mechanism uses this signal to modulate occupancy updates before corrupted measurements are accumulated into the map. Experiments on a real mobile robotic platform equipped with an Intel RealSense D435 and a Jetson Orin Nano show that the proposed method substantially reduces false obstacle insertion and improves free-space preservation under real reflective-floor and glass-surface conditions, while introducing only modest computational overhead. These results indicate that treating glare as a measurement-reliability problem provides a practical and lightweight solution for improving costmap correctness and navigation robustness in safety-critical indoor environments.

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