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Deep learning, Inertial Measurements Units, and Odometry: Some Modern Prototyping Techniques for Navigation Based on Multi-Sensor Fusion

Abstract : This thesis deals with state estimation for vehicles that are equipped with various sensors such as cameras and iner- tial measurement units. The Kalman filter is a widely used tool that estimates the state of a dynamical system, which raises theoritical questions for the nonlinear systems present in navigation, and that relies on physical models and pa- rameters that need to be efficient tuned by the user. This thesis contributes to Kalman filtering for navigation, where the contributions focus on two different manners. The first manner consists in building on the recent invariant extended Kalman filter to address challenging issues, namely the inconsistency of extended Kalman filter for the problem of simultaneous localization and mapping, navigation with vision sensors, and the more general question of Kalman filtering on manifolds. The second manner consists in using recent tools from the field of artificial intelligence, namely deep learining, to improve Kalman filters, notably to relate sensors’ measurements to the state, and to tune the filter efficiently, that is, using machine learning techniques to find a dynamical tuning strategy of the Kalman filter parameters that best matches the data and that is also able to provide new information to the filter. The thesis thus introduces differents filtering algorithms and deep neural networks whose implementation are made open-source.
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https://hal-mines-paristech.archives-ouvertes.fr/tel-03006062
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Submitted on : Sunday, November 15, 2020 - 10:25:42 AM
Last modification on : Wednesday, November 18, 2020 - 3:11:09 AM
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  • HAL Id : tel-03006062, version 1
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Martin Brossard. Deep learning, Inertial Measurements Units, and Odometry: Some Modern Prototyping Techniques for Navigation Based on Multi-Sensor Fusion. Automatic. ISMME, 2020. English. ⟨tel-03006062⟩

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