Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps

TL;DR

This paper introduces a novel depth reliability modeling and fusion approach to mitigate glare effects in RGB-D sensors, significantly enhancing navigation safety in reflective environments.

Contribution

It presents a lightweight depth reliability estimator and a reliability-guided fusion method to reduce false obstacles caused by glare in costmaps.

Findings

Reduces false obstacle insertion in glare-prone environments

Improves free-space preservation in occupancy maps

Operates with modest computational overhead

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.