It is taught by Christian LAUGIER, Agostino MARTINELLI and Dizan VASQUEZ from Inria.
This course is designed around a real-time decision architecture using Bayesian approaches. It covers topics such as:
- Sensor-based mapping and localization: presentation of the most popular methods to perform robot localization, mapping and to track mobile objects.
- Fusing noisy and multi-modal data to improve robustness: introduction of both traditional fusion methods as well as more recent approaches based on dynamic probabilistic grids.
- Integrating human knowledge to be used for scene interpretation and decision making: discussion on how to interpret the dynamic scene, predict its evolution, and evaluate the risk of potential collisions in order to take safe and efficient navigation decisions.
The course is primarily intended for students with an engineering or masters degree, but any person with basic familiarity with probabilities, linear algebra and Python can benefit from it.
The course can also complement the skills of engineers and researchers working in the field of mobile robots and autonomous vehicles.
This MOOC was created by Inria’s MoocLab, as part of the uTOP project ( http://utop.fr/ – http://utop.inria.fr/). uTOP is an IDEFI project that aims to create a demonstrator for increasing research visibility through out training.
Track record and links:
Session 1 : May 18 to July 21, 2015
Session 2 : February 8 to March 27, 2016
- Infographic : Figures from the 2nd session of the MOOC
- course presentation page on FUN
- The videos and the slides of the MOOC can be downloaded on Canal U