Summary of Robust Collaborative Perception Without External Localization and Clock Devices, by Zixing Lei et al.
Robust Collaborative Perception without External Localization and Clock Devices
by Zixing Lei, Zhenyang Ni, Ruize Han, Shuo Tang, Dingju Wang, Chen Feng, Siheng Chen, Yanfeng Wang
First submitted to arxiv on: 5 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 paper proposes a novel approach for achieving spatial-temporal coordination among multiple agents in collaborative perception tasks. Traditional methods rely on external hardware-based signals, which can be vulnerable to noise and attacks. In contrast, the authors suggest recognizing geometric patterns within perceptual data to align the agents’ spatial-temporal coordinates. The proposed system, called FreeAlign, uses a graph neural network to identify common subgraphs between agents, enabling accurate relative pose and time estimation. FreeAlign is evaluated on real-world and simulated datasets, demonstrating comparable performance to systems relying on precise localization and clock devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine many robots or cameras working together to create a better picture of the world. To do this, they need to agree on where each other is and when something happens. Usually, we use special devices to help them figure this out, but these devices can be faulty or even hacked. Instead, researchers propose using patterns in the data itself to align everyone’s spatial-temporal coordinates. This allows the agents to work together without relying on external devices. The new system, called FreeAlign, works by analyzing the shapes and relationships between objects detected by each agent. It’s tested on real-world and simulated scenarios and performs just as well as more traditional methods. |
Keywords
» Artificial intelligence » Graph neural network
