8 accepted papers
Label distribution learning (LDL) is a novel paradigm that describe the samples by label distribution of a sample. However, acquiring LDL dataset is costly and time-consuming, which leads to the birth of incomplete label distribution learning (IncomLDL). All the previous IncomLDL methods set the de
Vision is a critical sensing modality for underwater robots. However, a limited field of view (FoV) significantly hampers vision-based distributed swarm control, making it difficult for the swarm to maintain cohesion and preventing the emergence of typical collective behaviors. In this study, we pre
Label distribution learning (LDL) is an effective method to predict the relative label description degree (a.k.a. label distribution) of a sample. However, the label distribution is not a complete representation of an instance because it overlooks the absolute intensity of each label. Specifically,…
This article presents a nonlinear estimator on matrix Lie group that performs a fast unscented transformation with natural evolution of sigma points from a geometric perspective. Different from the existing methods, the proposed method preserves the original dynamic equations on the manifold, which
Multi-robot coverage planning has gained significant attention in recent years. In this letter, we introduce a novel approach called APF-CPP (Artificial Potential Field Based Multi-Robot Online Coverage Path Planning) to enhance the collaboration of multi-robot systems to accomplish coverage tasks i
Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Numerous LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structur…
The configuration of most robotic systems lies in continuous transformation groups. However, in mobile robot trajectory tracking, many recent works still naively utilize optimization methods for elements in vector space without considering the manifold constraint of the robot configuration. In this
Label distribution learning (LDL) is an effective method to predict the label description degree (a.k.a. label distribution) of a sample. However, annotating label distribution (LD) for training samples is extremely costly. So recent studies often first use label enhancement (LE) to generate the est…