Distribution line operation object pose estimation method based on point cloud

Abstract

The invention discloses a distribution line operation object pose estimation method based on point cloud. The method comprises the following steps of collecting the point cloud data of an operation scene including an object to be measured; cutting the point cloud; setting the average distance between the point clouds as a confidence interval, and then removing the point clouds outside the confidence interval; performing semantic segmentation on the point cloud to obtain the operation object point cloud as a to-be-registered point cloud set P; establishing a three-dimensional model of the operation object with the pose to be estimated, and converting the three-dimensional model into a PCD format of the point cloud so as to construct a point cloud model of the operation object with the poseto be estimated, and taking the point cloud model as a reference point cloud set Q; performing coarse registration on the point cloud set P to be registered and the reference point cloud set Q to enable the reference coordinate systems of the point cloud set P and the reference point cloud set Q to be consistent so as to obtain an initial pose of the operation object; and correcting the initial pose to obtain a final pose. According to the method, the pose measurement result of the operation object can be quickly and accurately obtained in the power distribution line with a more chaotic background, and the method has the higher robustness for illumination change.

Description

technical field [0001] The invention belongs to the field of pose measurement of a working object of a live working robot, in particular to a method for estimating the pose of a working object of a power distribution line based on a point cloud. Background technique [0002] With the vigorous development of robot technology, the status of robots in modern production and life is becoming more and more important. Introducing live working robots into the power industry to replace manual power maintenance and repair work can effectively avoid casualties during live work, and can greatly improve the operating efficiency of power maintenance and repair. [0003] At present, most of the live working robots developed at home and abroad for maintenance of power distribution lines require operators to remotely operate the robotic arm to perform the task at high altitude or with the help of video surveillance. The positioning accuracy of the target is low, which makes it very difficul...

AI Technical Summary

Problems solved by technology

The positioning accuracy of the target is low, which makes it very difficult to perform operations that require precise positioning, such as replacing lightning arresters and connecting lead wires. Therefore, it is necessary to measure the pose of the work object

However, in the live working site, the environment is more complex, there are many equipment and appliances, and these equipment and appliances have a single color, which is not easy to distinguish from the background environment. These factors make it difficult to measure the target pose

Currently commonly used pose measurement methods, such as the LINEMOD-based method for RGBD images, use stable gradient information and normal vector features to obtain a better pose estimation method, but the robustness to lighting conditions is not strong

When the lighting in the scene changes, the color distribution of the object also changes, which leads to the instability of the model and the failure of the object pose measurement

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