Pose estimation algorithm for disordered stacked workpieces based on central point pair features
In response to the pose estimation accuracy degradation caused by mutual occlusion of workpieces in disordered stacking scenarios,a six-dimensional pose estimation algorithm based on central point pair features was proposed.Initially,a simulated physical environment was created to emulate disordered stacking scenarios,where point cloud targets were randomly positioned in multiple poses,generating a dataset with ground truth labels for the feature extraction network.Subsequently,an offline global feature description was constructed using central point pair features.Then,for the scenario of disordered stacked workpieces in the online stage,the dynamic graph convolutional neural network(DGCNN)algorithm was employed to extract central feature score from the point cloud,determining the feature scores for identifying the object's central point.This central point was then utilized as a reference point for the enhancing point pair feature(PPF)algorithm.Finally,algorithm performance verification was conducted using the IPA dataset and self-collected scene data.Experimental results show that,the proposed algorithm reduced the randomness in reference point selection,resulting in an average accuracy improvement of 19.5 percentage points in scenarios with 30 workpieces compared with the original PPF algorithm.Moreover,the average runtime was reduced by approximately 29.00％across five different workpiece scenarios.
pose estimationfeature matchingcentral point extractiondeep learning
万方数据
维普
