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Cours scientifiques - INF657G : Navigation for Autonomous systems

Descriptif

Drones and robots must create maps of their surroundings to plan their movement and navigate. This course presents the robotic platforms and the most common sensors (vision, Lidar, intertial units, odometry …) and the different components of navigation: control; obstacle avoidance; localization; mapping (SLAM) and trajectory planning as well as filtering (Kalman filter, particle filtering, etc.) and optimization techniques used in these fields.

Format des notes

Numérique sur 20

Littérale/grade réduit

Pour les étudiants du diplôme M2 Système Cyber Physique

L'UE est acquise si note finale transposée >= C
  • Crédits ECTS acquis : 2.5 ECTS

Pour les étudiants du diplôme M1 Data AI - Data and Artificial Intelligence

Le coefficient de l'UE est : 2.5

Pour les étudiants du diplôme MScT-Artificial Intelligence and Advanced Visual Computing

Le rattrapage est autorisé (Note de rattrapage conservée)
    L'UE est acquise si note finale transposée >= C
    • Crédits ECTS acquis : 2 ECTS

    La note obtenue rentre dans le calcul de votre GPA.

    Programme détaillé

    - Course intro / organization
    - Introduction to mobile robotics.
    - Presentation of the different types of control architectures. Navigation approaches.
    - The sensors of mobile robotics and their use.
    - Map-based navigation. Environment representations.

    - Classification and presentation of the different localization methods. Direct localization methods
    - Position tracking methods. Iterated Closest Point.
    - Practical Work 01: ICP with a laser rangefinder

    - Localization by position tracking, Kalman filtering.
    - Practical work 02: Kalman filtering for robot localization

    - Particle filtering for robot localization.
    - Practical work 03: Particle filtering for robot localization

    - Classification and presentation of the different mapping methods. Kalman filtering mapping.
    - Practical work 04 : EKF SLAM

    - Optimization-based mapping methods.
    - Practical work 05 : Graph SLAM

    - Path planning for robotics.
    - Practical work 06 : RRT path planning

    Mots clés

    Mobile Robotics, Localization, Mapping, SLAM, path planning
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