Feature Detection and Classification in Buried Pipes using LiDAR: Dataset with Camera-LiDAR Synchronisation

LiDAR–Camera SynchronisationThis repository contains data collected from a Minicam crawler robot navigating confined pipe environments, equipped with a front-facing camera and a 2D LiDAR sensor. The pipe network includes two 2.5-meter concrete pipe sections, two 2-meter clay pipe sections and two 1-meter manholes. The pipe network has a total length of 11 meters and a diameter of 300 mm.
The collection was created to address challenges of accurate navigation, mapping, feature detection and classification within feature-sparse pipe networks.
The dataset includes:
Visual data captured during robot navigation (MP4 videos).2D LiDAR data provided both as raw ROS2 bag recordings and pre-extracted CSV scans.Ground-truth annotations describing pipe structural features (joints, manholes, clean path).Algorithm predictions generated by statistical models presented in our CCWI paper cited below.Synchronised outputs combining camera frames with corresponding LiDAR scans.A README file describing the repository structure, dataset contents, and usage instructions.This repository also provides tools and scripts for synchronising 2D LiDAR scans with camera video for underground crawler inspections. The goal is to align both camera and LiDAR streams in time.
CitationIf you use this dataset, please cite the CCWI 2025 paper:
Karnezis, Aristeidis; Worley, Rob; Blight, Andy; Anderson, Sean; Horoshenkov, Kirill; Mihaylova, Lyudmila (2025). Feature Detection and Classification in Buried Pipes using LiDAR Technology. The University of Sheffield. Conference contribution. https://doi.org/10.15131/shef.data.29920931