Kanza-79

Ainstein Vehicle Imaging Radar

Ainstein K-79 Vehicle Radar

Ainstein Vehicle Imaging Radar K-79 is a cutting-edge automotive imaging radar sensor. We’ve purpose-built the K-79 for self-driving industrial trucks, tractors, specialty vehicles, and other emerging autonomous automobile applications.

2d cloud mapping

2D point cloud mapping

customizable

Detects both moving and stationary objects

customizable

Highly customizable

Ainstein’s new K-79 Vehicle Imaging Radar brings unprecedented understanding of the real-time operating environment for autonomous specialty vehicles operating in rough and challenging conditions.The K-79 captures details of the surrounding scene moving objects such as vehicles, bikes, or pedestrians, as well as stationary objects including light poles, railings, etc.

Ainstein’s K-79 Vehicle Imaging Radar pinpoints the location of detected objects with range, azimuth & elevation, and velocity data, and generates a 2D point cloud mapping of the surroundings. The K-79 also has the ability to output a 3D shape of detected objects.

Applications

  • Self-driving industrial trucks, self-driving sweeper, self-driving forklift
  • Self-driving passenger cars, self-driving shuttle
  • Self-driving construction vehicles, self-driving excavator
  • Self-driving lawn mower, self-driving snow remover
  • Robotics
  • More!

The K-79 offers lidar-like image quality, paired with the high reliability under bad weather and low light conditions that only radar systems can provide.
Ainstein’s K-79 Vehicle Imaging Radar even offers target classification, allowing for distinguishing of vehicles vs. pedestrians, opening doors to unlimited possibilities for self-driving X:

  • self-driving excavator,
  • self-driving snow remover,
  • self-driving shuttle,
  • self-driving sweeper,
  • self-driving forklift,
  • self-driving lawn mower,
  • and more.

Combining advanced radar system design and low-cost commercial radar components, Ainstein brings high resolution radar imaging to commercial applications, something that was previously available only to the scientific community.

K-79 is the first to market commercial imaging radar system optimized and validated in low light and even incredibly dusty underground conditions.

It has been deployed to enable unmanned operation of Borer Miners and Bridge Conveyor to automate excavation and removing excavated material, a much safer and highly productive solution for underground mining.

K-79 is designed to be easily integrated into diverse applications. It supports multiple high-speed output connections commonly required for passenger cars, construction vehicles, and robotics. Its ability to output raw radar data makes it an ideal choice for sensor fusion development.

See the K-79 deployed in a real-world environment

Interested in connecting about the K-79?

Ainstein partners with industry leaders in various vertical markets, combining real-life application knowledge and advanced radar systems technology to offer the sensing solution that works for your unique needs!

Looking for technical specifications? Download the datasheet here.

Ainstein partners with industry leaders in various vertical markets, combining real-life application knowledge and advanced radar systems technology to offer the sensing solution that works for your unique needs!

Once you enter your information the link to download the PDF will appear here.

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Robot Operating System (ROS) compatible

Ainstein is proud to offer ROS support for its products.

ROS, or Robot Operating System, is a powerful and flexible open source framework that enables software development for robotic systems.

Ainstein’s ROS-compatible smart radar systems allow you to take control for streamlining integration of our radar sensors into your own custom applications.

Ainstein’s expert engineering team provides custom solutions for applications ranging from ROS integration to full-stack autonomy. Contact us today to discuss your own custom ROS integration.

Looking for codebase? Check out Ainstein’s GitHub by clicking here.