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

翻译：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央节点，共同为移动平台提供感知服务。所提出的基于扫描模式与地面接触特征的层次聚类激光雷达-相机融合方法，提升了拥挤环境下的节点内感知能力。该系统还具备延迟感知的全局感知功能，以同步和聚合跨节点数据。为验证我们的方法，我们引入了室内行人追踪数据集，该数据集由两个室内传感器节点采集的数据编译而成。与基线方法相比，我们的实验结果表明，该系统在检测精度和抗延迟鲁棒性方面均有显著提升。数据集可在以下仓库获取：https://github.com/NingMingHao/MVSLab-IndoorCooperativePerception