通过可靠性评估减少反光干扰，提升导航地图准确性。
Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps
- 基于像素级可靠性建模，判断深度测量可信度。
- 反光场景下误检障碍物减少67%，空旷区域保留率提升41%。
- 轻量设计适合实时机器人导航，尤其适合玻璃/反光地面环境。
镜面反光在反光地板和玻璃表面常导致RGB-D深度测量出现孔洞与尖刺，这些误差在占用栅格代价地图中累积为持久的虚假障碍物。本文提出一种基于显式深度可靠性建模的抗反光代价地图构建方法。采用轻量级深度可靠性图（DRM）估计算器，预测像素在镜面干扰下的测量可信度；再通过可靠性引导融合（RGF）机制，在错误数据累积前动态调节占据更新。在搭载Intel RealSense D435与Jetson Orin Nano的真实移动机器人平台上实验表明，该方法显著减少误检障碍物（降低67%），提高自由空间保留率（提升41%），同时仅引入适度计算开销。结果表明，将反光视为测量可靠性问题，可为安全关键室内环境提供高效、轻量的代价地图纠错方案。
原文摘要 · Abstract (English)
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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