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    Post Graduate Program in Sensor Fusion

    BY
    Skill Lync

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    Mode

    Online

    Duration

    32 Weeks

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    certificate availability

    No

    The syllabus

    Course 1: Localization, Mapping and SLAM using Python

    Week 01 - Introduction to Localization
    • Methods of performing localization (With the example of the self-driving car)
    • Various sensors are used for localization (LiDAR, RADAR, GNSS, INS, wheel encoders)
    • Sensing models for sensors used in practice that increase the presence of uncertain results in imperfect models
    • Downloading Python and Jupyter notebook
    • Numpy and matplotlib
    Week 02 - Probability Theory Refresher
    • Concepts such as random variable, random vectors, density function, joint density, marginal density, conditional independence
    • Probability Density Function (PDF), gaussian, multivariate gaussian
    • Variance and covariance
    • Bayes rule with multiple variables
    • Random variables in robotics state estimation
    Week 03 - Probabilistic modelling & Bayesian Filtering
    • Probabilistic models for perception and state transition
    • Fitting model from Data, Maximum Likelihood Estimate (MLE)
    • Bayesian filter, Markov assumption
    • Kalman filters and Particle filters
    Week 04 - Kalman Filter
    • Kalman filter and its derivation from Bayes filter
    • Kalman Gain (with an example)
    • Kalman filter assumptions and optimality
    Week 05 - Extended Kalman Filter and Unscented Kalman Filter
    • Limitations of Kalman filter
    • Variants that overcome the limitations
    • Jacobians in Extended Kalman filter
    Week 06 - Particle Filter (aka Monte Carlo Localization)
    • Map-based localization
    • Derivation of particle filter from the Bayes filter.
    • PF handling non-linearity.
    • Properties of particle filter
    Week 07 - Multi Sensor Fusion
    • Loosely coupled and tightly coupled techniques in Extended Kalman filtering
    • Extension of state vector to accept multiple inputs
    Week 08 - Introduction to Mapping and SLAM
    • Map generation process from the perspective of self-driving car
    • SLAM problem (similar to chicken and egg problem)
    • The simplest implementation of SLAM – EFF SLAM and loop closure
    Week 09 - Graph SLAM
    • Difference between offline SLAM and online SLAM Motivate Graph-based modelling of a SLAM problem
    • The mathematical formulation of Graph SLAM and how it is used offline to generate a map of the environment
    Week 10 - FastSLAM
    • Particle filter-based SLAM
    • Mathematical derivation and comparison with three techniques of SLAM discussed
    Week 11 - Other Implementations for SLAM
    • Factor graph and pose graph formulations of SLAM problem
    • Use of camera as another sensing source in SLAM [Optional]
    • An example of how to factor graph is implemented on a drone fitted with a downward-facing camera [Optional]
    • Robot Operating System – what it is and important concepts (Publisher, Subscriber, topics, message, and more)
    Week 12 - ROS Extra Lecture
    • Cmake & catkin system,
    • Advance concepts such as action server and transform tree
    • Importance of open source
    • Monte Carlo Localization as a system that is deployed in simulation

    Course 2: Radar Sensor Processing

    Week 01- Introduction to ADAS
    • Introduction to ADAS
    • Different types of ADAS
    • Applications of ADAS
    Week 02- Autonomous Driving (AD)
    • Introduction of Autonomous Driving (AD)
    • Levels of Autonomous Driving
    • The overall architecture of AD
    • How Computer Vision is used in AD?
    • What’s Deep Learning's role in AD?
    Week 03- Understanding of Basic sensors used in AD
    • Sensors roles in Autonomous driving
    • How different sensors work in AD:
    • Camera Sensors
    • Radar
    • LiDAR
    • Sensor Fusion - Camera, LiDAR, Radar
    • How safety is achieved with multiple sensors
    Week 04- Introduction to RADAR sensor
    • What is Radar?
    • Automotive Radar
    • How Radar Sensor Looks
    • Radar sensor on Vehicle
    Week 05- Radar Signal Processing
    • Introduction
    • Components of Radar Signal processing
    • Range Equation
    • FMCW
    • FMCW – Terms and Definitions
    • Measurement of Range (Distance)
    • Measurement of Doppler Velocity
    • Measurement of Angle/Angle of Arrival
    • Measurement of RCS
    Week 06- Advance Radar Signal Processing
    • Introduction
    • Range FFT and Doppler FFT
    • Angle FFT and RD Map
    • Clutter Removal and CFAR
    • Final Detection List
    Week 07- Radar Technical details
    • Introduction
    • Pulse Repetition Frequency
    • Duty Cycle
    • Dwell Time/ Hits per Scan
    • The Radar Equation
    • Free-Space Path Loss
    • Derivation of the Radar Equation
    • Radar Cross-Section
    • Losses
    Week 08- Radar Devices and roles
    • Classification of Radar System
    • Radar Devices and their functionalities
    • Role of Radar Sensors
    • Importance of Radar
    • Automotive Radar
    • Radar Signal Processing in automotive systems
    Week 09- Radar Data Processing
    • Introduction
    • Data processing
    Week 10- Environment Setup
    • Setting up things/ pre-requirements for coding and setting up the environment.
    • Getting Git Ready
    • Git basics
    • Creating repo
    • Hands-on the Git setup.
    • Installation of Compilers
    • Device Setup
    Week 11- RADAR+ AI
    • Deep Learning: PointNet and PointPillars
    • Discussion of the Network and how to change Lidar data for the network.

    Course 3: Introduction to Camera Systems Using C++

    Week 1 - Camera Construction
    • Introduction to Geometrical Construction
    • Introduction to Optical Construction 
    • Introduction to Camera Types
    • Camera Sensor Types – CCD, CMOS 
    • Camera Sensor Types – RGGB, RCCB, RCCC
    • Different Lens Types – Normal vs Fisheye
    • Optical Parameters – Exposure Time, Shutter, White Balance, Gain
    Week 2 - Camera Models
    • Different Camera Models
    • Pin hole model, Perspective model, fisheye model
    • Lens Distortion – Barrel /Radial, Pin Cushion
    • Depth Of Field , Field of View 
    • Effects on changing aperture
    Week 3 - Camera Calibration
    • Camera Calibration
    • Introduction to Camera Parameters
    • Calibration Techniques 
    • Calibration for Intrinsic vs Extrinsic 
    • Image Undistortion
    Week 4 - Projective Geometry
    • Introduction to Projective Geometry
    • What is Lost / Preserved ?
    • Vanishing Lines & Points
    • Dimensionality Reduction
    • World to Image Projection
    • Orthographic Projection
    Week 5 - Stereo Vision
    • Introduction To Stereo Vision
    • Basic Idea of Stereo
    • Epipolar Geometry
    • Image rectification
    • Stereo Correspondence
    • Disparity Maps
    • Depth Maps
    Week 6 -Camera Systems
    • Low FOV Long range cameras
    • Stereo Camera 
    • FLIR  Camera
    • Fisheye Camera – Continental
    • Camera Parameters
    • Different Uses for each of them
    Week 7 - Image Pre-Processing
    • Image Color Spaces
    • Color Space conversions (RAW -> RGB, RGB-> GRAYSCALE, RGB->YUV , …)
    • Image Digitization, Sampling, Quantization
    • Image Interpolation, Extrapolation
    • Image Normalization
    • Image Noise – Salt and Pepper noise, Gaussian Noise , Impulse Noise
    • Image Erosion/Dilution
    Week 8 - Image Processing -1 (Transformations)
    • Basic Transformations and Filtering
    • Domain Transformations
    • Noise Reduction
    • Filtering as Cross Correlation
    • Convolution
    Week 9 - Image Processing -2
    • Basic Image Filtering and Detection techniques
    • Corners Detection
    • Edge Detection
    • Contour Detection
    • Image Thresholding Histogram
    • Histogram Equalization
    Week 10 - Image Processing -3
    • Features and Image Matching
    • Image Features, Invariant Features (Geometrical, Photometric Invariance) 
    • Image Descriptors
    • HOG
    • SIFT  
    • SURF 
    • Image Stitching
    Week 11 - Image Processing - 4
    • Introduction to Structure from Motion (SFM)
    • Epipolar Constraint and Essential Matrix
    • 3d Reconstruction 
    • Bundle Adjustment
    • SVD approach to SFM
    • SLAM example
    Week 12- Introduction to Embedded Systems
    • Camera Interfaces. Ex : GMSL, LVDS
    • Communication Protocol – I2C 
    • Camera Initialization Sequence 
    • Automated Exposure Gain (AEG) Control 
    • Vision Processing Units (VPU) 
    • Graphic Processing units

    Course 4: Math behind Machine Learning & Artificial Intelligence using Python

    Week - 01 Basic concepts
    • Sets
    • Subsets
    • Power set
    • Venn Diagrams
    • Trigonometric Functions
    • Straight lines
    • A.M, G.M, and H.M
    • Concepts of Vectors
    Week - 02 Permutation & Combinations
    • Introduction to permutation and combinations
    • Basics of permutation and combinations
    • Fundamental principle of counting
    • Permutations
    • Combinations 
    Week - 03 Statistics - I
    • First business moment
    • Second business moment
    • Third business moment
    • Fourth business moment
    Week - 04 Probability
    • Introduction to probability
    • Random experiments
    • Conditional probability
    • Joint probability
    Week - 05 Statistics – II
    • Z Scores
    • Confidence interval
    • Correlation
    • Covariance
    Week - 06 Probability - II
    • Introduction to probability - II
    • Uniform Distribution
    • Normal Distribution
    • Binomial Distribution
    • Poisson Distribution
    Week - 07 Likelihood (for Logistic regression)
    • Introduction to likelihood statistics
    • Odds
    • Log odds
    • Maximum likelihood vs probability
    • Logistic regression
    Week - 08 Gradient descent (for Linear & Logistic regression)
    • Loss function
    • Cost function
    • The gradient descent for linear regression
    • The gradient descent for logistic regression
    Week - 09 Linear Algebra (for PCA)
    • Matrices
    • Types of matrices
    • Operation on matrices
    • Eigen values
    • Eigen vectors
    Week - 10 Derivatives (for Neural network)
    • Derivatives
    • Intuitive ideas of derivatives
    • Increasing & decreasing function
    Week - 11 Backpropagation (for Deep learning)
    • Chain rule
    • Maxima & minima
    • Back propagation
    • The cost function for deep learning
    Week - 12 Python
    • Basics of Python
    • If else
    • For loop
    • Data types

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