Camera matrix enables three-dimensional vision

March 27, 2019 //By Christoph Hammerschmidt
Camera matrix enables three-dimensional vision
For applications in autonomous driving, a research consortium has developed a camera matrix which, as a novel sensor, opens up a wide range of applications in the areas of autonomous driving and manual assembly processes in industry. The research group has now presented an interim report.

At the presentation of their "half-time milestone", the consortium partners presented a first prototype of the camera. The camera matrix consists of sixteen individual cameras arranged in a square, which together serve not only as imaging devices, but also as distance meters. The matrix measures about three by three centimeters and was made from a high-resolution full-format sensor with microlenses in front of it. It captures the scene from sixteen slightly offset perspectives to calculate a depth image. A small but efficient processor, embedded in the camera system, allows depth information to be calculated in real time. The captured color and depth data serve as input for downstream applications. Due to its compact design, the camera can also be integrated into small components as a non-contact sensor.

Oliver Wasenmüller, consortium leader and co-initiator of the DAKARA project (“Design and Application of a Compact, Energy-efficient and Configurable Camera Matrix for Spatial Analysis”) explains: "The camera matrix impresses with the advantages of a passive depth sensor combined with a compact design and a high frame rate. The 3D depth images always contain valuable additional information to the color images, which make the application of many algorithms possible in the first place or bring them to a robustness that allows a professional application.

The new system has been extensively tested in two different application scenarios by DAKARA's industrial partners:

During experiments as a rear-view camera through partner Adasens Automotive GmbH, the system recognized even finer structures such as kerbs or posts during autonomous parking. It detects the vehicle's environment both spatially and metrically and thus provides much more precise 3D information than conventional ultrasonic sensors today.

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