通过协同感知提升室内移动平台的实时定位精度，有效应对延迟问题。
Enhancing Indoor Mobility with Connected Sensor Nodes: A Real-Time, Delay-Aware Cooperative Perception Approach
- 基于扫描模式与地面接触特征的分层聚类，提升单节点感知能力。
- 实现跨节点数据同步与聚合，延迟容忍度显著优于基线方法。
- 适合智能机器人、巡检系统等动态室内场景应用。
本文提出一种面向动态室内环境的实时、延迟感知协同感知系统，由多模态传感器节点网络与中心节点组成，共同为移动平台提供感知服务。提出的基于扫描模式与地面接触特征的分层聚类激光雷达-相机融合方法，显著提升了复杂人群环境下的单节点感知性能。系统还具备延迟感知的全局感知机制，可实现节点间数据的同步与聚合。为验证方法有效性，作者构建了室内行人追踪数据集，基于两个室内传感器节点采集的数据。实验表明，该系统在检测准确率和抗延迟能力上均显著优于基线方法。相关数据集已开源：https://github.com/NingMingHao/MVSLab-IndoorCooperativePerception。
原文摘要 · Abstract (English)
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
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