用统计模型精准分离道路激光雷达中的背景与前景。
A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction
- 基于高斯网格建模背景空间分布，可解释性强。
- 在仅需少量背景数据下仍优于现有方法。
- 适合低算力设备，适用于各类车载激光雷达。
我们提出一种完全可解释且灵活的统计方法，用于道路侧激光雷达数据的背景剔除，以提升自动驾驶中基于基础设施的感知能力。该方法引入高斯分布网格（GDG），通过仅包含背景的扫描数据建模背景的空间统计特性，并设计过滤算法利用该表示将激光雷达点分类为前景或背景。该方法支持多种激光雷达类型，包括多线360度及微机电系统（MEMS）传感器，且能适应不同配置。在公开的RCooper数据集上评估显示，其在准确性和灵活性上均超越现有先进方法，即使在背景数据极少的情况下也表现优异。高效实现使其能在低资源硬件上可靠运行，支持大规模实际部署。
原文摘要 · Abstract (English)
We present a fully interpretable and flexible statistical method for background subtraction in roadside LiDAR data, aimed at enhancing infrastructure-based perception in automated driving. Our approach introduces both a Gaussian distribution grid (GDG), which models the spatial statistics of the background using background-only scans, and a filtering algorithm that uses this representation to classify LiDAR points as foreground or background. The method supports diverse LiDAR types, including multiline 360 degree and micro-electro-mechanical systems (MEMS) sensors, and adapts to various configurations. Evaluated on the publicly available RCooper dataset, it outperforms state-of-the-art techniques in accuracy and flexibility, even with minimal background data. Its efficient implementation ensures reliable performance on low-resource hardware, enabling scalable real-world deployment.
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