Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps
For mobile robots operating in indoor environments with reflective surfaces, this work provides a lightweight, practical solution to improve costmap correctness and navigation robustness.
The paper tackles the problem of specular glare corrupting RGB-D depth measurements, which leads to false obstacles in navigation costmaps. It proposes a reliability-guided fusion method that reduces false obstacle insertion and improves free-space preservation, achieving practical performance on a real robot with modest computational overhead.
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.