Towards multi-view sputum smear quality classification

Towards High Efficient Long-Horizon Planning With Expert-Guided Motion-Encoding Tree Search

Robust Semi-Supervised Point Cloud Registration via Latent GMM-Based Correspondence

Due to the fact that point clouds are always corrupted by significant noise and large transformations, aligning two point clouds by deep neural networks is still challenging. This paper presents a semi-supervised point cloud registration (PCR) method for accurately estimating point correspondences and handling large transformations using limited prior datasets. Firstly, a modified autoencoder is introduced as the feature extraction module to extract the distinctive and robust features for the downstream registration task. Unlike optimization-based pairwise PCR strategies, the proposed method treats two point clouds as two implementations of a Gaussian mixture model (GMM), which we call latent GMM. Based on the above assumption, two point clouds can be regarded as two probability distributions. Hence, the PCR of two point clouds can be approached by minimizing the KL divergence between these two probability distributions. Then, the correspondence between the point clouds and the latent GMM components is estimated using the augmented regression network. Finally, the parameters of GMM can be updated by the correspondence and the transformation matrix can be computed by employing the weighted singular value decomposition (SVD) method. Extensive experiments conducted on both synthetic and real-world data validate the superior performance of the proposed method compared to state-of-the-art registration methods. These experiments also highlight the method’s superiority in terms of accuracy, robustness, and generalization.

Motion Planning of Manipulator by Points-Guided Sampling Network

MO-Transformer: A Transformer-Based Multi-Object Point Cloud Reconstruction Network

AN AUTONOMOUS EYE-IN-HAND ROBOTIC SYSTEM FOR PICKING OBJECTS IN A SUPERMARKET ENVIRONMENT WITH NON-HOLONOMIC CONSTRAINT, 352-361.

Unified Intention Inference and Learning for Human–Robot Cooperative Assembly

Towards Minimally-Intrusive Navigation in Densely-Populated Pedestrian Flow

Towards Components-of-Interest Feedback Control and State Estimation of Robotic Manipulator

Vision based autonomous gap-flying-through using the micro unmanned aerial vehicle