Spatial synchronization in roadside scenarios is essential for integrating
data from multiple sensors at different locations. Current methods using
cascading spatial transformation (CST) often lead to cumulative errors in
large-scale deployments. Manual camera calibration is insufficient and requires
extensive manual work, and existing methods are limited to controlled or
single-view scenarios. To address these challenges, our research introduces a
parallel spatial transformation (PST)-based framework for large-scale,
multi-view, multi-sensor scenarios. PST parallelizes sensor coordinate system
transformation, reducing cumulative errors. We incorporate deep learning for
precise roadside monocular global localization, reducing manual work.
Additionally, we use geolocation cues and an optimization algorithm for
improved synchronization accuracy. Our framework has been tested in real-world
scenarios, outperforming CST-based methods. It significantly enhances
large-scale roadside multi-perspective, multi-sensor spatial synchronization,
reducing deployment costs.

我们的研究引入了基于并行空间变换的框架用于大规模、多视角、多传感器场景，实现了路边场景的空间同步，减小了累积误差，并通过深度学习、地理定位线索和优化算法提高了同步精度。此框架在真实场景中进行了测试，在大规模路边多视角、多传感器空间同步方面表现优于现有方法，降低了部署成本。