A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction
This addresses infrastructure-based perception for automated driving, though it appears incremental as it builds on existing statistical approaches.
The researchers tackled background subtraction in roadside LiDAR data for automated driving by developing a fully interpretable statistical method using a Gaussian distribution grid and filtering algorithm, which outperformed state-of-the-art techniques on the RCooper dataset with minimal background data.
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.