Memory-Augmented Point Cloud Registration Network for Bucket Pose Estimation of the Intelligent Mining Excavator

Bucket pose estimation is a key technology for the intelligent mining excavator to realize automatic excavation and loading. The existing estimation methods mainly include two categories. The sensor measurement methods install sensors to detect the bucket pose, which suffer from large cumulative error and short service life caused by strong vibrations. The traditional registration methods estimate the bucket pose by registering its point clouds with the iterative closest point method, which easily fall into a local optimum. Recently, point cloud registration networks have been studied for object pose estimation based on balanced training samples. However, since the bucket speed changes obviously during excavation, the number of bucket samples at different poses is imbalanced. As a result, point cloud registration networks will easily forget the bucket features at some poses which have small samples, also called nondominant pose features, during learning. This will reduce the accuracy of bucket pose estimation. To handle the forgetting problem caused by the sample imbalance, we propose a novel memory-augmented registration network (MARNet) for bucket pose estimation. The MARNet consists of an encoder, a dual-pose-memory module (DPMM), and a decoder. The encoder extracts the global pose features from the input point clouds. The DPMM learns and memorizes the prototypical pose features covering all the samples by using the memory units at the training phase. Then, nondominant pose features are augmented by retrieving the stored prototypical pose features at the testing phase. At last, the decoder calculates the final poses. Our method can effectively alleviate the forgetting problem and strengthen the generalization by using the DPMM. The experimental results demonstrate that our method increases the estimation accuracy of rotation and translation by at least 1.49° and 0.021 m, respectively, for the bucket poses with small samples. This contributes to the development of intelligent mining excavators.