对比了路边感知中重复与非重复扫描的LiDAR性能，发现非重复扫描性价比更高。
Which LiDAR scanning pattern is better for roadside perception: Repetitive or Non-repetitive?
- 构建仿真数据集，对比不同扫描模式的点云分布差异。
- 非重复扫描与128线重复扫描检测性能相当，但成本更低。
- 适合预算有限且需高效部署的智能交通系统设计者。
基于激光雷达的路边感知是高级智能交通系统的核心。尽管已有大量研究关注激光雷达在基础设施中的最优布置，但不同扫描模式对感知性能的影响仍相对缺乏系统研究。传统重复扫描（如机械式/固态式）与新兴非重复扫描（如棱镜式）导致不同距离下的点云分布差异，显著影响目标检测和环境理解效果。为此，本文在CARLA仿真环境中构建了“InfraLiDARs' Benchmark”数据集，同步采集采用两种扫描范式的路侧激光雷达数据。基于该基准，系统分析了不同扫描模式的感知能力，并评估其对多种主流3D目标检测算法的影响。结果表明，非重复扫描激光雷达与128线重复扫描激光雷达在多种场景下检测性能相当；尽管非重复扫描感知范围较小，但因其成本低廉，具备较高性价比。本研究为不同路边应用选择最优激光雷达扫描模式及适配算法提供了依据，并公开发布“InfraLiDARs' Benchmark”数据集以推动后续研究。
原文摘要 · Abstract (English)
LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrastructure, the profound impact of differing LiDAR scanning patterns on perceptual performance remains comparatively under-investigated. The inherent nature of various scanning modes - such as traditional repetitive (mechanical/solid-state) versus emerging non-repetitive (e.g. prism-based) systems - leads to distinct point cloud distributions at varying distances, critically dictating the efficacy of object detection and overall environmental understanding. To systematically investigate these differences in infrastructure-based contexts, we introduce the "InfraLiDARs' Benchmark," a novel dataset meticulously collected in the CARLA simulation environment using concurrently operating infrastructure-based LiDARs exhibiting both scanning paradigms. Leveraging this benchmark, we conduct a comprehensive statistical analysis of the respective LiDAR scanning abilities and evaluate the impact of these distinct patterns on the performance of various leading 3D object detection algorithms. Our findings reveal that non-repetitive scanning LiDAR and the 128-line repetitive LiDAR were found to exhibit comparable detection performance across various scenarios. Despite non-repetitive LiDAR's limited perception range, it's a cost-effective option considering its low price. Ultimately, this study provides insights for setting up roadside perception system with optimal LiDAR scanning patterns and compatible algorithms for diverse roadside applications, and publicly releases the "InfraLiDARs' Benchmark" dataset to foster further research.
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