Pipe environment elbow features extracting based on 2D Point Cloud

For autonomous pipeline robot applications, extracting the features in the pipeline environment such as the 90-degree elbow can greatly reduce the error of the pipeline robot odometer and improve the accuracy of the real-time positioning for autonomous pipeline robot. At present, iterative calculations are used in most of the features extracting methods such as least squares method, but with the huge amount of point cloud data, the computational complexity of these methods is high, and the amount of computation limits the application on embedded robots. For this problem, a network framework For the pipe environment is proposed in this article, which is only for point cloud data input. Based on You Only Look Once v4-tiny(YOLOv4-tiny), a rapid 2D standard detection network framework for images expanding, the discrete 2D point cloud data in the form of bird's eye view is encoded in low-resolution as the input of the net and point of interest (POI) is detected and segmented for the extraction of the elbow features and the accurate estimation of the real-time positioning for the pipeline robot. Our experiments in narrow pipe environment show that compared with the current point cloud feature extraction methods, the proposed method is faster and more accurate.