利用互联传感器节点增强室内移动性：一种实时、延迟感知的协同感知方法
Enhancing Indoor Mobility with Connected Sensor Nodes: A Real-Time, Delay-Aware Cooperative Perception Approach
AI总结：
提出一种面向动态室内移动平台的实时、延迟感知协同感知系统，通过多模态传感器网络与中心节点，结合基于分层聚类的Lidar Camera Fusion及延迟感知全局感知技术，显著提升了拥挤环境下的检测准确性与抗延迟鲁棒性。
摘要：
本文提出了一种新颖的实时、延迟感知协同感知系统，专为在动态室内环境中运行的智能移动平台设计。该系统包含一个多模态传感器节点网络和一个中心节点，它们共同为移动平台提供感知服务。所提出的基于扫描模式和地面接触特征的分层聚类的Lidar Camera Fusion（激光雷达相机融合），改善了拥挤环境下的节点内感知。该系统还具有延迟感知的全局感知功能，以同步和聚合跨节点数据。为了验证我们的方法，我们引入了Indoor Pedestrian Tracking（室内行人跟踪）数据集，该数据集由两个室内传感器节点捕获的数据编译而成。与基线相比，我们的实验表明在检测准确性和对延迟的鲁棒性方面有显著改善。该数据集可在以下仓库获取：https://github.com/NingMingHao/MVSLab-IndoorCooperativePerception
英文摘要：
This paper presents a novel real-time, delay-aware cooperative perception system designed for intelligent mobility platforms operating in dynamic indoor environments. The system contains a network of multi-modal sensor nodes and a central node that collectively provide perception services to mobility platforms. The proposed Hierarchical Clustering Considering the Scanning Pattern and Ground Contacting Feature based Lidar Camera Fusion improve intra-node perception for crowded environment. The system also features delay-aware global perception to synchronize and aggregate data across nodes. To validate our approach, we introduced the Indoor Pedestrian Tracking dataset, compiled from data captured by two indoor sensor nodes. Our experiments, compared to baselines, demonstrate significant improvements in detection accuracy and robustness against delays. The dataset is available in the repository: https://github.com/NingMingHao/MVSLab-IndoorCooperativePerception
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
