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Topics to be covered include: Bayesian inference, state of the art SLAM and VAN approaches, and bundle adjustment. Depending on progress, some of the following advanced topics will be also briefly covered: multi-robot cooperative localization and mapping, active SLAM and belief space planning, intro/overview of recent deep learning approaches.
We encourage student participation from multiple faculties/departments at the Technion.
- The course syllabus and more information can be found here.
- The course is managed via Piazza.
- Let me know (by email) if you do not have a Technion email.
Course tentative schedule (topics and schedule often change from one semester to another):
| Lecture week | Topic |
|---|---|
| 1 | Introduction, 3D rigid transformations, 6 DOF Poses |
| 2 | Probability basics, Bayesian inference, Extended Kalman filter |
| 3 | Projective camera geometry, Feature detection and matching |
| 4 | Structure from Motion I, Multiple view geometry, Bundle adjustment |
| 5 | SLAM and VAN |
| 6 | Graphical Models, iSAM |
| 7 | iSAM, visual-inertial SLAM |
| 8 | Active SLAM, Belief space planning |
| 9 | Midterm exam |
| 10 | Cooperative navigation and SLAM I |
| 11 | Cooperative navigation and SLAM II |
| 12 | Project presentations |
| 13 | Project presentations |
Topics to be covered include: Probabilistic inference, MDP and POMDP formulation, belief space planning (BSP), information-theoretic costs, search- and sampling-based planning, application to autonomous navigation and active SLAM, Gaussian processes, informative planning and active perception, an overview of (deep) learning-based approaches.
We encourage student participation from multiple faculties/departments at the Technion.
- The course syllabus and more information can be found here.
- The course is managed via Piazza.
- Let me know (by email) if you do not have a Technion email.
Course tentative schedule (topics and schedule often change from one semester to another):
| Lecture week | Topic |
|---|---|
| 1 | Introduction, Probabilistic inference, Environment representations |
| 2 | MDP & POMDP formulation, Belief space planning problem |
| 3 | Belief space planning problem |
| 4 | Search-based and Sampling-based planning |
| 5 | Search-based and Sampling-based planning |
| 6 | Information-theoretic costs/rewards |
| 7 | Application to autonomous navigation and active SLAM |
| 8 | Midterm exam |
| 9 | Informative planning |
| 10 | MDP and POMDP revisited, data-driven approaches |
| 11 | MDP and POMDP revisited, data-driven approaches |
| 12 | Project presentations |
| 13 | Project presentations |
