This paper describes the development of an in-pipe inspection robot system designed for large-diameter water pipes. The robot is equipped with a Magnetic Flux Leakage (MFL) sensor module. The robot system is intended for pipes with diameters ranging from 900 mm to 1200 mm. The structure of the in-pipe inspection robot consists of the front and rear driving parts, with the inspection module located centrally. The robot is powered by 22 motors, including eight wheels with motors positioned at both the bottom and the top for propulsion. To ensure that the robot’s center aligns with that of the pipeline during operation, lifting units have been incorporated. The robot is equipped with cameras and LiDAR sensors at the front and rear to monitor the internal environment of the pipeline. Pipeline inspection is conducted using the MFL inspection modules, and the robot’s driving mechanism is designed to execute spiral maneuvers while maintaining contact with the pipeline surface during rotation. The in-pipe inspection robot is configured with wireless communication modules and batteries, allowing for wireless operation. Following its development, the inspection robot underwent driving experiments in actual pipelines to validate its performance. The field test bed used for these experiments is approximately 1 km in length. Results from the driving experiments on the field test bed confirmed the robot’s ability to navigate various curvatures and obstacles within the pipeline. It is posited that the use of the developed in-pipe inspection robot can reduce economic costs and enhance the safety of inspectors when examining aging pipes.

This study introduces Cylindrical Scan Context (CSC), a novel LiDAR descriptor designed to improve robustness and efficiency in GPS-denied or degraded outdoor environments. Unlike the conventional Scan Context (SC), which relies on azimuth–range projection, CSC employs an azimuth–height representation that preserves vertical structural information and incorporates multiple physical channels—range, point density, and reflectance intensity—to capture both geometric and radiometric characteristics of the environment. This multi-channel cylindrical formulation enhances descriptor distinctiveness and robustness against viewpoint, elevation, and trajectory variations. To validate the effectiveness of CSC, real-world experiments were conducted using both self-collected coastal–forest datasets and the public MulRan–KAIST dataset. Mapping was performed using LIO-SAM with LiDAR, IMU, and GPS measurements, after which LiDAR-only localization was evaluated independently. A total of approximately 700 query scenes (1 m ground-truth threshold) were used in the self-collected experiments, and about 1200 scenes (3 m threshold) were evaluated in the MulRan–KAIST experiments. Comparative analyses between SC and CSC were performed using Precision–Recall (PR) curves, Detection Recall (DR) curves, Root Mean Square Error (RMSE), and Top-K retrieval accuracy. The results show that CSC consistently yields lower RMSE—particularly in the vertical and lateral directions—and demonstrates faster recall growth and higher stability in global retrieval. Across datasets, CSC maintains superior DR performance in high-confidence regions and achieves up to 45% reduction in distance RMSE in large-scale campus environments. These findings confirm that the cylindrical multi-channel formulation of CSC significantly improves geometric consistency and localization reliability, offering a practical and robust LiDAR-only localization framework for challenging unstructured outdoor environments.