Indoor Localization Technology


INDOOR localization technology for tracking the position and orientation of moving assets (forklifts, AGV/AMR, robots...), in real time and with centimeter accuracy.

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Edge AI and Vision Alliance

Onit is a technological member of the Edge AI and Vision Alliance, a collective American reality that fosters international partnerships with the aim of connecting technology providers and manufacturing companies, thus accelerating the diffusion of products based on Edge AI and Computer Vision.

Description


Dragonfly CV uses Video Recognition of the features of the ceiling to locate moving asset (forklift, AGV/AMR, robot...), at a lower cost than LiDAR and UWB.

Dragonfly CV has an accuracy of 10cm and the data refresh rate is above 20Hz.

Documented APIs allow the System Integrator to collect historical or real-time data for tracking the position and orientation of the asset equipped with Dragonfly CV technology.

Declaration of Conformity
On.Plant/RTLS is approved according to European quality and safety standards, as evidenced by the declaration of conformity.

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Advantages


  • Maximum localization of moving assets
  • Lower costs than LiDAR and UWB
  • Unique and compact solution
  • Real-time data collection
  • Centimeter accuracy
  • Durable and long-lasting device
  • All-in-one solution
  • Integration with our RTLS software

Dragonfly: using computer vision to locate any moving asset in an indoor environment

The different variants of Indoor Localization Technology that improve your work

Dragonfly CV Standard kit

The compact device is integrated with the RTLS software and consists of:

  • Mini PC
  • Camera
  • Standard Case

Dragonfly CV Rugged kit

The compact device is integrated with the RTLS software and consists of:

  • Mini PC
  • Camera
  • Rugged Case with active cleaning system to remove dust and raindrops

Dragonfly CV Frozen kit

The compact device is integrated with the RTLS software and consists of:

  • Mini PC
  • Camera
  • Industrial Case with Heating System

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