# Enhancing Indoor Mobility with Connected Sensor Nodes: A Real-Time, Delay-Aware Cooperative Perception Approach

arXiv: https://arxiv.org/abs/2411.02624 · HTML: https://mlsanity.com/paper/2411.02624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [TH"OR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset](https://mlsanity.com/paper/1909.04403/) (2019)
- [Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset](https://mlsanity.com/paper/2303.14880/) (2023)
- [Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance](https://mlsanity.com/paper/1708.00163/) (2017)

## Abstract (arXiv)

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