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PA - C8 - INF657G : Navigation for Autonomous systems


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 Artificial Intelligence and Advanced Visual Computing

L'UE est acquise si note finale transposée >= C
  • Crédits ECTS acquis : 2 ECTS

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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