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 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
7	iSAM
8	iSAM, visual-inertial SLAM
9	Active SLAM, Belief space planning
10	Cooperative navigation and SLAM I
11	Cooperative navigation and SLAM II
12	Project presentations
13	Project presentations

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 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
3	Belief Space Planning
4	Information Theoretic Costs
5	MDP and POMDP Approaches I
6	MDP and POMDP Approaches II
7	MDP and POMDP Approaches III
8	Informative Planning and Active Perception I
9	Informative Planning and Active Perception II
10	Search-based Planning
11	Sampling-based Planning
12	Project Presentations
13	Project Presentations

Autonomous Planning under Uncertainty (0970252)

The course provides mathematical tools and approaches for solving planning under uncertainty problems in partially observable domains in the context of AI and robotics.

Topics to be covered include: Probability space, Probabilistic inference, MDP and POMDP problem formulations, belief space planning, information-theoretic costs, nonparametric inference, offline MDP and POMDP approaches (dynamic programming, value iteration, policy iteration, Alpha vectors), online MDP (forward search, branch & bound, sparse sampling, Monte Carlo tree search), online POMDP approaches (POMCP, POMCPOW, PFT-DPW), robust and risk-averse planning under uncertainty.

Teaching

Vision Aided Navigation (086761)

Autonomous Navigation and Perception (086762)

Autonomous Planning under Uncertainty (0970252)