in der Wissenschaft

Nerians 3D-Tiefenwahrnehmung in der Wissenschaft

Unsere Stereovision-Technologie wurde bereits in zahlreichen Forschungsprojekten eingesetzt. Nachfolgend finden Sie eine Liste der wissenschaftlichen Publikationen von uns und unseren Kunden, bei denen unsere Technologie zur Anwendung gekommen ist.

Selbstverständlich sind wir jederzeit daran Interessiert bei Forschungsprojekten nach Möglichkeit mitzuwirken oder diese zu unterstützen.

GreenTeam Universität Stuttgart

Derzeit unterstützen wir das GreenTeam der Universität Stuttgart im Bereich autonomes Fahren.

MIT & Delft Driverless

Nerian unterstützte das gemeinsame Team der TU Delft und des MIT beim Formula Student Wettbewerb 2019. Das Team erreichte einen hervorragenden 3. Platz von insgesamt 20 Teilnehmern.

Publikationen von Nerian

Real-Time Stereo Vision on FPGAs with SceneScan

We present a flexible FPGA stereo vision implementation that is capable of processing up to 100 frames per second or image resolutions up to 3.4 megapixels, while consuming only 8 W of power. The implementation uses a variation of the Semi-Global Matching (SGM) algorithm, which provides superior results compared to many simpler approaches. The stereo matching results are improved significantly through a post-processing chain that operates on the computed cost cube and the disparity map. With this implementation we have created two stand-alone hardware systems for stereo vision, called SceneScan and SceneScan Pro. Both systems have been developed to market maturity and are available from Nerian Vision GmbH.

Publiziert bei Forum Bildverarbeitung 2018, pp. 339–350.

SP1: Stereo Vision in Real Time

Stereo vision is a compelling technology for depth perception. Unlike other methods for depth sensing, such as time-of-flight or structured light cameras, stereo vision is a passive approach. This makes this method suitable for environments with bright ambient lighting, or for situations with multiple sensors within close proximity to one another. The reason why stereo vision is not used more widely is that it requires a vast amount of computation. To overcome this burden, Nerian Vision Technologies introduces the SP1 stereo vision system. This stand-alone device is able to handle the required processing by relying on a built-in FPGA.

Publiziert bei MuSRobS@ IROS, 2015, pp. 40–41.

Publikationen von Kunden mit Bezug auf SceneScan

  • Jiang, P., Osteen, P., Wigness, M. & Saripalli, S. (2020). RELLIS-3D Dataset: Data, Benchmarks and Analysis. arXiv:2011.12954 [cs.CV]. [Link]
  • Yildiz, E., Brinker, T., Renaudo, E., Hollenstein, J. J., Haller-Seeber, S., Piater, J., & Wörgötter, F. A. (2020). Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines. In Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS 2020), p. 17-27. [Link]
  • Hinze, C., Zürn, M., Wnuk, M., Lechler, A. & Verl, A. (2020). Nonlinear Trajectory Control for Deformable Linear Objects based on Physics Simulation. In the 46th Annual Conference of the IEEE Industrial Electronics Society (IECON), p.310-316. [Link]
  • Strobel, K., Zhu, S., Chang, R. & Koppula, S. (2019). Accurate, Low-Latency Visual Perception for Autonomous Racing: Challenges, Mechanisms, and Practical Solutions. Technical report. [Link]
  • Hinze, C., Wnuk, M. & Lechler, A. (2019). Harte Echtzeit für weiche Materialien. atp magazin, v. 61, n. 11-12, p. 112-119. [Link]
  • Vrba, M., Heřt, D. & Saska, M. (2019). Onboard Marker-Less Detection and Localization of Non-Cooperating Drones for Their Safe Interception by an Autonomous Aerial System. IEEE Robotics and Automation Letters, 4(4), 3402-3409. IEEE. [Link]

Publikationen von Kunden mit Bezug auf Stereovision IP-Core

  • Kartha, A., Sadeghi, R., Barry, M. P., Bradley, C., Gibson, P., Caspi, A., Roy, A. & Dagnelie, G. (2020). Prosthetic Visual Performance Using a Disparity-Based Distance-Filtering System. Translational Vision Science & Technology, 9(12), 27-27. [Link]
  • Fütterer, R., Schellhorn, M., & Notni, G. (2019). Implementation of a multiview passive-stereo-imaging system with SoC technology. In Photonics and Education in Measurement Science 2019 (Vol. 11144, p. 111440Q). International Society for Optics and Photonics. [Link]

Publikationen von Kunden mit Bezug auf SP1

  • Erz, M. (2018). Computer vision based pose detection of agricultural implements without a priori knowledge of their geometry and visual appearance. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-6). IEEE. [Link]
  • Buck, S., & Zell, A. (2019). CS:: APEX: A Framework for Algorithm Prototyping and Experimentation with Robotic Systems. Journal of Intelligent & Robotic Systems, 94(2), 371-387. [Link]
  • Hanten, R., Kuhlmann, P., Otte S. & Zell, A. (2018). Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2000-2006). IEEE. [Link]
  • Dubey, G., Madaan, R., & Scherer, S. (2018). DROAN-Disparity-Space Representation for Obstacle Avoidance: Enabling Wire Mapping & Avoidance. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6311-6318). IEEE. [Link] [PDF]
  • Zilly, J., Buhmann, J. M., & Mahapatra, D. (2017). Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Computerized Medical Imaging and Graphics, 55, 28-41. [Link] [PDF]

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