system architecture, algorithms, software and hardware · system architecture, algorithms, software...

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www.pegasusprojekt.de Testing and Safeguarding – Stand 19a System Architecture, Algorithms, Software and Hardware iMAR Navigation develops and provides in PEGASUS solutions for real-time moni- toring and validation of test runs via pose estimation and scene interpretation using INS/GNSS technology and binocular vision (with and without a-priori known maps). Challenge: Monitoring (real life) test runs on proving grounds using a multi-sensor system implemen- ted on the roof of all vehicles associated with the test run. Detection of ego pose deviations and scene interpretation. System Architecture: Implementation of multi-tier software architecture (driver, data, algorithms, visualiza- tion, appplication). Multi-Sensor Head: Incorporating of RTK INS/ GNSS, binocular vision and high performance mesh communication module. Algorithms: Visual Odometry for pose esti-mation, based on stereo image sequence analysis. Machine Learning (Convolu- tional Neural Networks) for semantic interpretation of vehicle surrounding. Scene reconstruction com- bined with Machine Learning for estimation of infrastruc- ture on proving grounds in order to estimate ego pose based on a priori generated maps. iMAR Doc-No. DOC171018146, Rev. 1.01, 10/2017 TOOLS FOR PROVING GROUND AND FIELD TESTS LOCALIZATION AND COLLISION AVOIDANCE

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Page 1: System Architecture, Algorithms, Software and Hardware · System Architecture, Algorithms, Software and Hardware iMAR Navigation develops and provides in PEGASUS solutions for real-time

www.pegasusprojekt.de

Testing and Safeguarding – Stand 19a

System Architecture, Algorithms, Software and Hardware

iMAR Navigation develops and provides in PEGASUS solutions for real-time moni-toring and validation of test runs via pose estimation and scene interpretation usingINS/GNSS technology and binocular vision (with and without a-priori known maps).

Challenge:• Monitoring (real life) test runs

on proving grounds using a multi-sensor system implemen-ted on the roof of all vehiclesassociated with the test run.

• Detection of ego posedeviations and sceneinterpretation.

System Architecture:• Implementation of multi-tier

software architecture (driver, data, algorithms, visualiza-tion, appplication).

Multi-Sensor Head:• Incorporating of RTK INS/

GNSS, binocular vision andhigh performance meshcommunication module.

Algorithms:• Visual Odometry for pose

esti-mation, based on stereo image sequence analysis.

• Machine Learning (Convolu-tional Neural Networks) forsemantic interpretation ofvehicle surrounding.

• Scene reconstruction com-bined with Machine Learning for estimation of infrastruc-ture on proving grounds in order to estimate ego posebased on a priori generatedmaps.

iMAR Doc-No. DOC171018146, Rev. 1.01, 10/2017

TOOLS FOR PROVING GROUND AND FIELD TESTS

LOCALIZATION AND COLLISION AVOIDANCE