Research on internal positioning method of pipeline robot based on data fusion and point cloud registration

Abstract

Limited by the shielding effect from the pipeline structure and materials, pipeline robots usually rely on sensors such as odometers and inertial measurement units (IMUs), resulting in significant error accumulation effect and difficulty in ameliorating positioning accuracy. In this paper, we present an internal positioning method in the pipeline based on data fusion and point cloud registration to improve this problem. First of all, the Extended Kalman Filter (EKF) fuses the odometer and IMU data, acquiring the estimated pose's initial value and calculating the derived position. Secondly, the conditional filtering of the known pipeline point cloud is carried out using the derived position to solve the inaccurate problem caused by the significant difference in the number of points between the two groups in point cloud matching. Eventually, the improved iterative closest point (ICP) algorithm is applied to the filtered point cloud and the data of the two line-laser sensors, obtaining accurate pose information of the robot. The proposed method enables the robot to locate only by its sensor data, which is especially suitable for use in pipelines where the environment is harsh and wireless signals are often not received. In our experiments, we first demonstrate the effectiveness and necessity of point cloud filtering, and then compare our algorithm with the traditional intralocalization algorithms under the Gazebo/ROS simulation platform. The results show that the positioning accuracy of our method is better than the traditional internal positioning method.