Post Graduate Program in Sensor Fusion

Skill Lync

Online

32 Weeks

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

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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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