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RoboticsIIITH / summer-sessions-2019

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summer-sessions-2019

A series of lectures and hands-on tutorials organized between the 6th of May 2019 and 20th of June 2019 to familiarize new lab entrants with the fundaments of different areas of robotics research that the lab conducts.

An important aspect of this session will be getting familiar with various robots and sensors present in the lab. This knowledge/skill will be critical as some of the assignments will involve using these systems to collect data and/or execute the algorithms.

Venue and Timings:

Tentative topics:

Lab Robots & other Hardwares: ROS (Robot Operating System), Cameras(Monocular, Stereo, RGBD), Lidars, IMUs, Flight controllers, Husky Robot, P3DX Robot, Bebeop Drone, GPS, etc.

Fundamentals of Linear Algebra, Calculus and Optimization: Vectors, Matrices, Vector and Matrix operations, Important matrices, Matrix decompositions, Gradient, Hessian, Linear and non-linear least squares, Unconstrained optimization methods, Lagrange multipliers.

Rigid Body Transformations: Rotation matrices, Homogeneous Transformation matrices, Rigid Body transformation, Composition of transformation by current-axis and fixed-axis conventions.

Geometric methods in Computer Vision: Projective geometry, Camera modelling, Camera Calibration, Two-view geometry, Triangulation, Resection, SfM, Visual odometry.

Deep learning for Computer Vision & Tensor Flow

Motion Planning: Robot modelling, Motion Planning overview, Sampling based planning, Variational methods for planning.

Reinforcement Learning: Markov Processes, Planning using Dynamic Programming, Value Iteration and Policy Iteration, Model Free RL(TD learning, SARSA, Importance Sampling), table Q-learning, Value Function Approximation(Incremental and Batch), Deep Q Networks(DQN), Policy Gradients(REINFORCE, Actor-Critic Methods). (Additionally we can also cover Deep RL(DQN, Double DQN, Asynchronous DQN, Bayesian RL, TRPO, PPO, Inverse RL etc.) if we have time).

Schedule

Date Topic Presenter Material Additional Reading
6-5-19 Intro to lab and sessions, Maths review Mithun Class Slide Matrix_Differentiation_Reference
7-5-19 Maths review contd. Mithun Class Slide Reference Materials
9-5-19 Rigid Body Transformations and Image Formation Gourav Class Slide Image Formation at end of slide
11-5-19 Rigid Body Transformations contd. Gourav Class Slide at end of slide
13-5-19 Camera Calibration Junaid slide Camera Intrinsics and Extrinsics
15-5-19 Multiple-view Geometry 1 - Overview, Intro to visual odometry, Feature detection and matching, Motion estimation Karnik Class slides
SIFT, F-matrix slides		 Original SIFT paper
MVG Ch. 9 & 10
17-5-19 Multiple-view Geometry 2 - Epipolar geometry, RANSAC Karnik Class slides MVG Ch. 9 & 11
MVG Sec. 4.6
Moving object detection paper
20-5-19 Stereo Mahtab TBD TBD
22-5-19 Multiple-view Geometry 3 - Triangulation, Resection, Bundle Adjustment Karnik Class slides MVG Ch. 12
E-PnP
Bundle adjustment
23-5-19 DL 1 Sarthak Basic review of ML and forward propagation TBD
24-5-19 DL 2 Shashank Back Propagation, CNN & Optimization Methods CS231n lecture slides
27-5-19 DL 3 Sarthak PyTorch Introduction and Coding TBD
28-5-19 DL 4 Shashank CNN Architectures, RNNs/LSTMS, object detection CS231n lecture slides
29-5-19 DL 4 Shashank Coding LSTMs and time series prediction TBD
1-6-19 Motion Planning Mithun Motion Planning overivew and Graph search methods Slides, Slides
3-6-19 Motion Planning Mithun Sampling based methods and Local Planning Slides
8-6-19 Trajectory Generation/ ROS Mithun, Gourav ROS Tutorial ETH ROS Course
9-6-19 ROS Gourav ROS Tutorial ETH ROS Course
19-6-19 RL Basics Kaustubh slides DeepMind Lectures

Assignments

Assignment No. Release Date Topic files Deadline
1 10-5-19 Linear Algebra, Optimization, Transformations Problem statement 14-5-19
2 15-5-19 DLT Calibration Problem statement 19-5-19
3 25-5-19 Two-view reconstruction Problem statement 29-5-19
4 27-5-19 Deep Learning_Assign1 Problem statement 3-6-19

References

Linear Algebra / Vector Calculus / Optimization:

Multiview Geometry:

Motion Planning:

Reinforcement Learning:

Contacts:

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