A Vendor-Agnostic LiDAR Data Conversion System with Multi-Signal Detection and Multi-Format Output

2026-06-22

AI Summary

This work addresses the incompatibility of PCAP data formats across LiDAR sensors from different manufacturers, which necessitates maintaining multiple independent parsing pipelines. To overcome this limitation, the authors propose a vendor-agnostic conversion framework that requires no manual configuration. By integrating high-performance C++ decoding with flexible Python-based parsing, the framework automatically identifies sensor vendors—including Ouster, Velodyne, Hesai, and Livox—and uniformly converts raw PCAP data into five widely adopted point cloud formats. Vendor recognition is achieved through a weighted six-dimensional file feature analysis, enabling high identification accuracy. Evaluated on a consumer-grade i3 platform, the system achieves processing speeds of 2.08 million (Ouster), 1.47 million (Velodyne), 110 thousand (Hesai), and 150 thousand (Livox) points per second, significantly enhancing both efficiency and interoperability.

Abstract

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

Problem

• LiDAR
• PCAP
• vendor-agnostic
• data conversion
• multi-format

Innovation

• vendor-agnostic
• multi-signal detection
• LiDAR data conversion
• PCAP parsing
• multi-format output