5 accepted papers
While Vision-Language-Action (VLA) models have achieved remarkable success in ground-based embodied intelligence, their application to Aerial Manipulation Systems (AMS) remains a largely unexplored frontier. The inherent characteristics of AMS, including floating-base dynamics, strong coupling betwe…
Point cloud data labeling is considered a time-consuming and expensive task in autonomous driving, whereas annotation-free learning training can avoid it by learning point cloud representations from unannotated data. In this paper, we propose AFOV, a novel 3D Annotation-Free framework assisted by 2D…
Efficiency is now a key challenge in automatic modulation classification (AMC), particularly in resource-constrained environments like mobile devices in 6G networks. This paper presents a framework based on the filter-bank channelizer (FBNet) for AMC, which gracefully strikes a balance between accur…
Autonomous driving datasets have played an important role in validating the advancement of intelligent vehicle algorithms including localization, perception and prediction in academic areas. However, current existing datasets pay more attention to the structured urban road, which hampers the explora…
Simultaneous Localization and Mapping (SLAM) has greatly assisted in exploring perceptually-degraded underground environments, such as human-made tunnels, mine tunnels, and caves. However, the recurring sensor failures and spurious loop closures in these scenes bring significant challenges to applyi…