Teaching


Vision Aided Navigation (086761): Fall 2020

The course focuses on fundamental topics in vision aided navigation (VAN) and simultaneous localization and mapping (SLAM), which are essential for autonomous operation in unknown, uncertain or dynamically changing environments.

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.

  • Course syllabus and information sheet can be found here.
  • The course is managed via Piazza.
    Let me know (by email) if you do not have a Technion email.

When: Sunday, 09:30-12:30
Where: TBD

Course tentative schedule (will probably change during the semester):

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

 


Autonomous Navigation and Perception (086762): Spring 2021

The course focuses on fundamental topics in planning under uncertainty (belief space planning) in the context of autonomous navigation and perception, considering online autonomous operation in unknown, uncertain or dynamically changing environments.

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 is managed via Piazza.
    Let me know (by email) if you do not have a Technion email.

When: Tuesdays, 14:30-17:30
Where: Zoom & Aerospace 241

Course tentative schedule (will probably change during the semester):

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