A vision-based assistance key differenciator for helicopters automonous scalable missions

Rémi Girard (1) , Sébastien Mavromatis (1) , Jean Sequeira (1) , Nicolas Belanger (2) , Guillaume Anoufa (3)

1 GMOD - MODélisation Géométrique
2 Airbus Helicopters
3 Capgemini

In the coming years, incremental automation will be the main challenge in the development of highly versatile helicopter technologies. To support this effort, vision-based systems are becoming a mandatory technological foundation for helicopter avionics. Among the different advantages that computer vision can provide for flight assistance , navigation in a GPS-denied environment is an important focus for Airbus because it is relevant for various types of missions. The present position paper introduces the different available SLAM algorithms, along with their limitations and advantages, for addressing vision-based navigation problems for helicopters. The reasons why Visual SLAM is of interest for our application are detailed. For an embedded application for helicopters, it is necessary to robustify the VSLAM algorithm with a special focus on the data model to be exchanged with the autopilot. Finally, we discuss future decisional architecture principles from the perspective of making vision-based navigation the 4th contributing agent in a wider distributed intelligence system composed of the autopilot, the flight management system and the crew.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Géométrie algorithmique [cs.CG]; Synthèse d'image et réalité virtuelle [cs.GR]; Traitement des images [eess.IV]

Rémi Girard, Sébastien Mavromatis, Jean Sequeira, Nicolas Belanger, Guillaume Anoufa. A vision-based assistance key differenciator for helicopters automonous scalable missions. 20th Towards Autonomous Robotic Systems Conference - TAROS, Jul 2019, Londres, United Kingdom. ⟨hal-02095960⟩