一键自动识别多品牌激光雷达数据并转换格式，省去配置烦恼。
A Vendor-Agnostic LiDAR Data Conversion System with Multi-Signal Detection and Multi-Format Output
- 通过六项信号特征加权判断激光雷达品牌，无需手动配置。
- 支持四类主流传感器，输出五种标准格式，统一处理流程。
- 在普通电脑上实测稳定运行，性能差距与厂商规格一致。
激光雷达通过测量激光脉冲飞行时间生成密集3D点云，是自动驾驶、机器人和大范围测绘的核心。原始PCAP文件是大多数3D感知流程的起点，但不同厂商（Ouster、Velodyne、Hesai、Livox）的内部包结构、UDP端口约定和编码方式差异显著，导致每个品牌需独立使用SDK、环境配置和转换流程。本研究提出一个统一管道：输入原始PCAP后，自动通过加权多信号方法分析六个独立特征以识别传感器类型。其中Ouster与Velodyne采用原生C++ SDK解码，而Hesai与Livox则基于Python的dpkt解析（因无开源SDK）。最终可通过单条命令输出至五种工业标准格式。实测显示：Ouster峰值达208万点/秒，Velodyne为147万点/秒，均使用原生C++解码；而Hesai与Livox分别为11万和15万点/秒，受Python层解析开销影响，在持续负载下性能衰减明显，8-10倍差距在多次测试中保持稳定。实验在消费级i3处理器、8GB内存设备上完成，无需任何厂商配置。
原文摘要 · Abstract (English)
LiDAR (Light Detection and Ranging) sensors capture the surrounding environment as dense 3D point clouds by measuring the time-of-flight of emitted laser pulses, making them foundational across autonomous vehicles, robotics, and large-scale mapping. PCAP (Packet Capture) files from these sensors are the starting point of most 3D perception pipelines, yet internal packet structures, UDP (User Datagram Protocol) port conventions and encoding schemes differ enough across manufacturers that no single tool reads them all. Ouster, Velodyne, Hesai, and Livox each require their own SDK (Software Development Kit), their own environment setup, and their own conversion workflow. Supporting all four means maintaining four disconnected pipelines with no shared infrastructure. The pipeline described here takes a raw PCAP as input and handles vendor identification automatically, scoring six independent file characteristics through a weighted multi-signal approach to determine the source sensor. C++ SDKs handle Ouster and Velodyne, while Hesai and Livox rely on Python-based dpkt parsing where no open source SDK exists. From there, a single command writes output to any of five industry-standard formats. We tested on real outdoor captures. Ouster peaks at 2.08M points per second, Velodyne at 1.47M, both running through native C++ packet decoding. Hesai and Livox land at 110K and 150K respectively, where Python-layer parsing introduces overhead that compounds under sustained load. The 8-10x gap held consistently across runs. Tested on a consumer-grade i3 with 8GB RAM, no vendor configuration required
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