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Founder's Pitch
"MapViT enables real-time predictions of radio quality maps for autonomous mobile robots in dynamic environments."
Commercial Viability Breakdown
Breakdown pending for this paper.
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Why It Matters
Real-time radio quality maps are crucial for robotic systems to maintain efficient and reliable operations in dynamic environments, improving navigation and communication by adapting to rapidly changing radio conditions.
Product Angle
Package MapViT as a software component integrated into robotic operating systems that provides continuous updates on radio quality alongside environmental mapping, enhancing robots' decision-making and autonomy.
Disruption
MapViT can replace traditional and sensor-heavy radio quality mapping systems that are not suitable for dynamic or unknown environments, offering a more adaptable and cost-effective solution.
Product Opportunity
The product targets industries relying on automated warehouses and robotic operations, such as logistics and manufacturing, where radio quality can impact efficiency. Customers include warehouse operators and robotics firms.
Use Case Idea
A commercial application could be a robotic fleet management system for warehouses that optimizes radio connectivity in real-time to improve autonomous navigation and task allocation.
Science
MapViT is a two-stage framework employing Vision Transformers (ViTs) to predict environmental changes and radio signal quality. It uses self-supervised learning on depth maps to comprehend the 3D structure and supervised fine-tuning to map these to radio quality predictions, improving efficiency and accuracy even with limited labeled data.
Method & Eval
The framework was tested by substituting ViTs with ML models like CNNs and MLPs to compare predictive accuracy, efficiency, and generalization. It demonstrated a balance between accuracy and computational efficiency, key for mobile platforms.
Caveats
Potential challenges include the initial integration complexity with existing robotic systems and the need for high-quality sensors for accurate environmental data capture.
Author Intelligence
Cyril Shih-Huan Hsu
Informatics Institute, University of Amsterdam
s.h.hsu@uva.nl
Xi Li
NEC Laboratories Europe
Xi.Li@neclab.eu
Lanfranco Zanzi
NEC Laboratories Europe
Lanfranco.Zanzi@neclab.eu
Zhiheng Yang
Informatics Institute, University of Amsterdam
z.yang@uva.nl
Chrysa Papagianni
Informatics Institute, University of Amsterdam
c.papagianni@uva.nl
Xavier Costa-Pérez
i2CAT Foundation, NEC Laboratories Europe, Catalan Institution for Research and Advanced Studies (ICREA)
xavier.costa@neclab.eu