Obstacle Detection for a Pipeline Point Cloud Based on Time Series and Neighborhood Analysis

Employing a robot to inspect the inner surface of the pipeline periodically is crucial to guarantee that the pipeline runs safely and reliably. Limited by the robot size and power. small three-dimensional measurement sensors with lower accuracy are frequently used with the robot to obtain environmental and navigation information. However, the quality of the pipeline point cloud acquired using such a sensor is substandard, making it challenging to reliably detect obstacles. Therefore, a point cloud processing approach according to time series and neighborhood analysis is proposed, which employs time and spatial distribution characteristics of obstacle point clouds and noise point clouds to remove noise and finally detects the obstacles by fitting the pipeline inner wall point clouds. The experiments reveal that the detection accuracy improves by 30 percentage points and the processing time is less than 1 s, meeting the requirements of the pipeline inspection robot.