A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction

• URL: http://arxiv.org/abs/2510.22390v1
• Date: Sat, 25 Oct 2025 18:18:10 GMT
• Title: A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction
• Authors: Aitor Iglesias, Nerea Aranjuelo, Patricia Javierre, Ainhoa Menendez, Ignacio Arganda-Carreras, Marcos Nieto,
• Abstract summary: We present a fully interpretable and flexible statistical method for background subtraction in roadside LiDAR data.<n>The method supports diverse LiDAR types, including multiline 360 degree and micro-electro-mechanical systems (MEMS) sensors.<n>It outperforms state-of-the-art techniques in accuracy and flexibility, even with minimal background data.
• Score: 3.9354551232038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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