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Phd - Urban Navigation Solution Based On An Optimized Fusion of Data From Video and Inertial Sensors
il y a 2 semaines
L’ENAC, École Nationale de l’Aviation Civile, est la plus importante des Grandes Écoles ou universités aéronautiques en Europe. Elle forme à un spectre large de métiers : des ingénieurs ou des professionnels de haut niveau capables de concevoir et faire évoluer les systèmes aéronautiques et plus largement ceux du transport aérien ainsi que des pilotes de ligne, des contrôleurs aériens ou encore des techniciens aéronautiques.
Ses laboratoires de recherche sont à la pointe de l’innovation et travaillent activement en coopération avec des universités internationales de haut niveau pour un transport aérien toujours plus sûr, efficace et durable.
L’ENAC est un établissement public à caractère scientifique, culturel et professionnel - grand établissement (EPSCP-GE), sous tutelle de la DGAC (Direction Générale de l’Aviation Civile), Direction du Ministère de la Transition Écologique et Solidaire. L’ENAC comprend une direction générale localisée à Toulouse et 8 sites en France.
- Pour soutenir sa dynamique en faveur de la promotion de la diversité, l’ENAC facilite l’accueil et l’intégration des travailleurs en situation de handicap._
**1. Context**
Navigation for autonomous and airport vehicles in urban or complex environments faces significant challenges due to the unavailability or degradation of GNSS (Global Navigation Satellite System) signals. Such degradations can result from environmental factors (urban canyons, tunnels, underground parking), intentional interference (jamming, spoofing), or a complete absence of signals in specific contexts. To address these limitations, integrating data from complementary sensors, such as video cameras and Inertial Navigation Systems (INS), has emerged as a promising solution to compensate for the shortcomings of traditional GNSS navigation systems. An alternative in urban environments is to use video sensors and rely on SLAM (Simultaneous Localization And Mapping) algorithms for road vehicle navigation. In the previously described case of GNSS signal degradation or unavailability, we propose to focus on a solution that integrates video and inertial measurements in a robust and reliable manner.
In the first phase, a Visual-Inertial SLAM (Simultaneous Localization And Mapping) algorithm will be developed based on an existing algorithm. This algorithm will integrate visual information captured by one camera with inertial data to simultaneously estimate position, orientation, and environmental mapping. Particular attention will be given to managing uncertainties, ensuring robust data fusion, and enabling the system to function in dynamic and complex environments.
The second phase of the thesis will focus on developing an integrity monitoring system. This module will ensure the reliability of the navigation outputs by detecting and assessing errors or anomalies in sensor data. A methodology will be implemented to detect unacceptable errors (monitors) and to compute protection levels adapted to this type of solution.
The core of the thesis will focus on the integrity analysis of video-based navigation and the prediction of its performance. The key tasks will include analyzing failure modes that induce large errors, nonlinearities, error correlation, and scenarios with a high number of biased measurements. These analyses are particularly challenging due to the complexity of the processing applied to video observations. The magnitude of errors at each stage of this complex processing must be quantified, and their propagation must be well understood. In particular, quantifying errors in feature localization from the observed video stream will be essential.
Additionally, the accuracy and integrity performance of the Video/INS solution will be compared with that of a GNSS/INS solution in different contexts. A detailed analysis will be conducted to evaluate the strengths and limitations of each approach, focusing on scenarios where GNSS is unavailable or degraded. These scenarios will include jamming and environments with blocked GNSS signals. The benefits of video in such cases will be thoroughly examined to highlight the added value of this hybrid solution.
This thesis, positioned at the intersection of inertial navigation, computer vision, and integrity monitoring research, offers an innovative scientific and technological contribution to addressing the challenges of navigation in urban and complex environments. The expected outcomes include a high-performance Video/INS hybridization algorithm, a tailored integrity monitoring method, and a rigorous evaluation framework for hybrid systems in diverse operational conditions.
**2. Objectives**
- Definition of an algorithm integrating Video (monocular cameras) and inertial data (accelerometers and gyroscopes).
- Validation and performance assessment of the solution in urban environments.
- 2. Development of an integrity monitoring solution:_
- Definition of performance criteria (accuracy, continuity, availability, robustness
