Vision-Based Navigation and Mapping

This course is for Graduate students and Undergraduate students in their last year of studies.

Inertial and dead reckoning navigation, probabilistic information fusion, vision aided navigation, simultaneous localization and mapping, IMU pre-integration, visual-inertial bundle adjustment, cooperative navigation and slam (centralized and distributed), active state estimation and belief-space planning. Learning Outcomes# After completion of the course the student will know how to#%

1. Develop and implement vision-aided navigation and slam algorithms.
2. Derive and implement probabilistic formulations for cooperative information fusion, van and slam.
3. Implement and solve standard bundle adjustment optimization using real imagery.
4. Develop algorithms for belief space planning.

SEMESTER START: March 25, 2026

Contact Hours per Week: 3

Day and Time: TBD

After completion of the course the student will know how to:

1. Develop and implement vision-aided navigation and slam algorithms.
2. Derive and implement probabilistic formulations for cooperative information fusion, van and slam.
3. Implement and solve standard bundle adjustment optimization using real imagery.
4. Develop algorithms for belief space planning.

SEMESTER START: March 25, 2026 Day & Time: TBD

Attend lectures

Final exam is a project presentation and a report

Random Signals #044202 or Random Processes in Aerospace Systems #086733 or Algorithms 1 #234247

Attend lectures; there is a final project that should be presented to the class