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

TL;DR

This paper introduces a real-time, delay-aware cooperative perception system with hierarchical clustering and sensor fusion for indoor mobility, validated by a new indoor pedestrian dataset showing improved detection accuracy.

Contribution

The paper proposes a novel delay-aware cooperative perception framework with hierarchical clustering and sensor fusion, and introduces a new indoor pedestrian dataset for validation.

Findings

Significant improvements in detection accuracy over baselines

Enhanced robustness against communication delays

Effective intra-node perception in crowded environments

Abstract

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