CP220-2025
Computational Techniques in Robotics and Autonomous Systems
Computational Techniques in Robotics and Autonomous Systems
The course will cover elements from these topics - with an emphasis on computational learning via Google Colab Workbooks.
Examples will largely from robotics/embedded AI, for e.g. estimation, perception, planning , controls etc.
The main topics will be:
Linear Algebra & Matrix Computations
Vectorspaces, Basis & Dimension, Norm, Transformations and Matrices, Matrix Operations, Rank, Linear Systems of Equations, Matrix Factorizations like LU, QR, Cholesky, Eigen Values & Vectors, Singular Value Decomposition, Condition number & Matrix Norms, Principal Component Analysis
Lie Algebra for Robot Motion
Manifolds & exponential maps, Special Orthogonal and Euclidean Groups SO(3), SE(3), Jacobians
Projective Geometry for Computer Vision
Pin Hole Model and Perspective projection, Homography and Estimation
Non-Linear Least Squares Optimization
Probability concepts for State Estimation
Multi-variate distributions, Moments, Entropy & Mutual Information, Bayesian Estimation, Kalman Filters, Sampling
Timing: M-W-F, 2-3pm. (First class Aug 4 2025)
Venue: Classroom 5, in TCS-X bldg
Teams link: Use this to join the MS teams group.