Summary of Enhancing Indoor Mobility with Connected Sensor Nodes: a Real-time, Delay-aware Cooperative Perception Approach, by Minghao Ning et al.
Enhancing Indoor Mobility with Connected Sensor Nodes: A Real-Time, Delay-Aware Cooperative Perception Approach
by Minghao Ning, Yaodong Cui, Yufeng Yang, Shucheng Huang, Zhenan Liu, Ahmad Reza Alghooneh, Ehsan Hashemi, Amir Khajepour
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel real-time, delay-aware cooperative perception system is designed for intelligent mobility platforms operating in dynamic indoor environments. The system combines a network of multi-modal sensor nodes and a central node to provide perception services to mobility platforms. A key innovation is the Hierarchical Clustering Considering the Scanning Pattern and Ground Contacting Feature based Lidar Camera Fusion, which improves intra-node perception in crowded environments. Additionally, the system features delay-aware global perception to synchronize and aggregate data across nodes. To evaluate this approach, an Indoor Pedestrian Tracking dataset was introduced, compiled from data captured by two indoor sensor nodes. Compared to baselines, our experiments demonstrate significant improvements in detection accuracy and robustness against delays. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new system that helps smart mobility platforms see their surroundings more clearly and quickly in busy indoor spaces. The system uses multiple sensors and a central hub to work together and provide information about the environment. It also has special features to handle delays and improve its performance. To test this, researchers created a dataset of data from two sensor nodes inside a building. The results show that this approach is better than others at detecting objects and handling delays. |
Keywords
» Artificial intelligence » Hierarchical clustering » Multi modal » Tracking
