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.
Diplôme(s) concerné(s)
Parcours de rattachement
Format des notes
Numérique sur 20Littérale/grade réduitPour les étudiants du diplôme Master M1 - Data and Artificial Intelligence
Pour les étudiants du diplôme Master M2 - Data & Artificial Intelligence
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
