By the end of this course, the students will be able to:
- Apply recursive state estimation through the Bayes Filter framework,
- Build probabilistic motion models for mobile robotic systems such as differential drive wheeled robots,
- Build probabilistic measurement (sensor) models for mobile robotic sensors such as laser scanners, cameras, proximity sensors etc,
- Design and implement estimation algorithms using the Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter and Particle Filter approaches,
- Solve Localization, Mapping and SLAM canonical robot perception problems using the aforementioned filter structures,
- State assumptions, limitations and practical issues and problems with probabilistic robotics algorithms.