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Localization, Mapping, and SLAM using Python

Learn about the simultaneous localization and mapping of autonomous robots using Python.

12 weeks long course | 100% Online

Learn from leading experts in the industry

Project based learning with 2 industry level projects that learners can showcase on LinkedIn.

Learn Key Tools & Technologies Python, C++

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Syllabus

This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

Course Overview

Programming sets the foundation for the behavior of Autonomous Vehicles and robots. Python programming is one such language that lets users automate robots to respond to a wide range of different conditions instead of having to teach them how to respond to each one. Autonomous robots are programmed to independently navigate their environment by using localization and mapping techniques. As autonomous robots and vehicles are a growing trend, knowing how to program them is a vital skill sought after in the industry. 

By taking this course, learners will 

  • Understand the basic concepts of localization and mapping, probability theory and its implications in robotics, the different algorithms employed in directing robot behavior, and the robot operating system. 
  • Extensively use Jupyter Notebook to write scripts that direct the robot’s behavior and consider all uncertainties it may face. 
  • Be exposed to the advanced techniques used in the autonomous vehicle and robotics industry. 
  • Complete challenges and projects designed by experts according to industry specifications that require practical application of the concepts learned throughout the course.

Who can enroll in this course?

This course comes under the autonomous domain, and can be taken by learners with an interest in statistics, calculus and programming experience in C++ and Python 

Course Syllabus

On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.

Week 1 - Introduction to Localization

When it comes to autonomous robots, the process of localization uses data from sensors to determine their behaviours and responses. Sensors in the system provide data inputs to the robot regarding the environment, following which it implements an algorithm to determine its upcoming behaviours. It is important to understand how vehicles and robots navigate their environment through localization.

This week will cover

  • Methods of performing localization (With the example of the self-driving car)
  • Different sensors 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 2 - Probability Theory Refresher and Probabilistic Modelling

As events that autonomous robots may encounter are not always predicted, the probability theory is considered to work through uncertainties in the environment. By applying algorithms in probability theory, all possible outcomes can be calculated. It is important to understand the basic concepts of probability theory, and the different functions and algorithms employed in determining possible outcomes.

This week will cover

  • Error sources: limitations of sensing - deterministic vs. non-deterministic
  • Augmented odometry model with error
  • Deviations because of error additions
  • Error propagation in IMU
  • 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
  • Random variables in robotics state estimation
  • Probability distribution

Week 3 - Bayesian Filtering

Probabilistic modelling takes the effect of random events into consideration to predict the occurrence of possible future outcomes. In robotics, Bayesian filtering is used for determining the possibilities of multiple perceptions to let the robot understand its relative position and orientation. It is important to understand how probabilistic modelling and Bayesian filtering work in the context of robotics.

This week will cover

  • Probabilistic generative laws
  • Definitions for state and environment
  • Concepts of belief, posterior
  • Probabilistic models for perception and state transition
  • Bayesian filter, Markov assumption
  • Kalman filters and Particle filters: Introduction

Week 4 - Kalman Filter

The Kalman filter works to reduce the overall error covariance and uncertainty by combining all the uncertainties in terms of the robot’s state and sensor measurements. It is essential in localization, trajectory tracking, identifying parameters, positioning, and the control of robots. It is important to understand how the Kalman filter works in the automation of robots.

This week will cover

  • Kalman filter and its derivation from Bayes filter
  • Kalman Gain (with an example)
  • Kalman filter assumptions and optimality

Week 5 - Extended Kalman Filter and Unscented Kalman Filter

The extended Kalman filter is applied for non-linear systems, while the Kalman filter is used for linear systems. By using the extended variant of the Kalman filter, limitations from using the Kalman filter alone can be overcome. The unscented Kalman filter is a non-linear Kalman filter. It is important to understand the different Kalman filters and when they are used.

This week will cover

  • Limitations of Kalman filter
  • Variants that overcome the limitations
  • Jacobians in Extended Kalman filter

Week 6 - Particle Filter (aka Monte Carlo Localization)

Particle filter localization, also called Monte Carlo Localization, is an algorithm implemented for localization. This algorithm predicts the position of the robot as it works its way through the environment by using a series of samples/particles. It is important to understand the implications of particle filter localization in the automation of robots.

This week will cover

  • Map-based localization
  • Derivation of particle filter from the Bayes filter
  • PF handling non-linearity
  • Properties of particle filter

Week 7 - Multi Sensor Fusion

Multi-sensor fusion is the means by which data from multiple sensors or sources are integrated to reduce the uncertainty of information from individual sources. This allows for accurate estimations of an autonomous robot’s location, position, and orientation. There are different methods and algorithms used in multi-sensor fusion, such as Kalman filtering and extended Kalman filtering. It is important to understand the algorithms used to combine data from multiple sensors.

This week will cover

  • Loosely coupled and tightly coupled techniques in Extended Kalman filtering
  • Extension of state vector to accept multiple inputs

Week 8 - Mapping

Autonomous robots and vehicles develop maps of their surroundings through mapping. In this process, sensors are used to gather information about the surrounding environment, and create a map with it. Mapping is a concept central to the development of autonomous vehicles and robots, making it imperative for learners to understand the process of map generation.

This week will cover

  • Map generation process from the perspective of self-driving car

Week 9 - Introduction to SLAM with EKF SLAM

SLAM (Simultaneous localization and mapping) is implemented for autonomous robots and vehicles to develop a map and localize the subject on that map. It programs the autonomous subject to navigate the environment on its own through localization and mapping. While localization uses input data from sensors to determine behaviours, mapping uses information from the sensors to develop a map for the subject to understand its positioning and orientation.

This week will cover

  • Explanation of how SLAM problems resemble chicken and egg proble
  • Explanation of simplest SLAM implementation - EFF, SLAM, and loop closure

Week 10 - Graph SLAM

Graph SLAM takes information regarding the locations of the autonomous robot as initial and relative motion constraints to provide the future probabilities of its movement. It is important to understand how graph-based SLAM works in the development of a robot’s position and orientation.

This week will cover

  • Difference between offline SLAM and online SLAM
  • Motivate graph based modeling of SLAM problem 
  • Explanation of mathematical formulation of Graph SLAM
  • Explanation of how Graph SLAM can be used offline to generate a map of environment

Week 11 - FastSLAM

FastSLAM is an algorithm that combines extended Kalman filters and particle filters to recursively predict full posterior distribution across the landmark locations and robot pose. It is important to understand how the FastSLAM algorithm works, and the mathematical derivations involved.

This week will cover 

  • Particle filter based SLAM
  • Mathematical derivation and comparison with three SLAM techniques

Week 12 - Other Implementations for SLAM and ROS Intro

 SLAM is used in mobile robotics to build and update the map of the environment that the robot has not explored by using sensors that get inputs from the surroundings. There are also other applications of SLAM. It is important to understand SLAM in the context of the Robotic Operating System (ROS).

This week will cover

  • Explanation of factor graph and pose graph formulations of SLAM problem
  • Explanation of how camera is used as sensing source in SLAM
  • Example of factor graph implementation on drone fitted with downward facing camera
  • ROS - what it is and important concepts (Publisher, subscriber, topics, message)

Our courses have been designed by industry experts to help students achieve their dream careers

Industry Projects

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.

Implementing an MCL Algorithm to Localize a Robot

In this project, learners will have to implement an MCL algorithm to simulate the localization of a robot equipped with INS and RFID tags. The various steps in the algorithm have to be written to demonstrate its effectiveness in mobilizing the robot. At the end of the project, learners will have to submit a report based on the analysis of the Bayesian Filtering and Kalman Filtering.

Implementing an Occupancy Grid Map Algorithm using Turtlebot

In this project, learners will have to implement an occupancy grid map algorithm using Turtlebot fitted with LiDAR and an accurate Odometry sensor. Learners will have a starter code including the setup of TurtleBot, using which they will be expected to write the occupancy grid map algorithm and simulate it using Gazebo. At the end of this project, learners will have to submit a report with a  detailed description of ROS and other implementations for SLAM.

Our courses have been designed by industry experts to help students achieve their dream careers

Ratings & Reviews by Learners

Skill-Lync has received honest feedback from our learners around the globe.

Google Rating
4.6

Master Localization, Mapping, and SLAM using Python Course

With the newest approach to manufacturing self-driving cars, OEMs started deploying the SLAM algorithm to make intelligent vehicles. The localization and mapping techniques provided vision to the cars by actively capturing data from sensors and cameras. The global computer vision engineers market is now expanding at a CAGR of 7% between 2022 and 2030, and industries need engineers with advanced skills. 

Skill-Lync's Localization, Mapping, and SLAM using Python is a 12-week online course. It will teach you local mapping and the SLAM algorithm. An industry expert with five years of experience in the trajectory optimization and RADAR domain has fine-tuned the course. As the SLAM robotics curriculum strictly adheres to industry standards, it will help you become job-ready. Two industry-level projects are included in the coursework to provide you with hands-on practice.

The fee for learning Localization, Mapping, and SLAM using Python ranges from INR 7,000 to INR 15,000 monthly for three months.

Who Should Take This Course?

The industry-oriented Localization, Mapping, and SLAM using Python course is for students and graduates of computer science and related streams of engineering. If you are curious about the technologies involved in automating and controlling a robot's behaviour, this course is for you. Experienced professionals looking for a career transition or expanding their skill sets can also opt for this course.

What will you learn?

The following topic will be dealt with in this course:

  • SLAM algorithm
  • Methods of performing localization
  • Probability Theory
  • Bayesian Filter
  • Kalman Filter
  • Map-based localization

Skills You Will Gain

  • The ability to implement MCL Algorithm, Occupancy Grid Map Algorithm and other algorithms for SLAM robotics.
  • Complete knowledge of standard industry practices and key technologies like Python and C++.
  • In-depth knowledge of localization and mapping techniques.

Key Highlights of The Program

  • Localization, Mapping, and SLAM using Python is a 12-week course.
  • Besides the course completion certificate for all participants, the top 5% of learners get a merit certificate.
  • You will get email and forum support to clear your doubts during the course.
  • Real-time industry-relevant projects will make your learning purposeful and practice-oriented.

Career Opportunities after Taking the Course

Upon completing the Localization, Mapping, and SLAM using Python course, numerous job opportunities will open up for you. Some exciting positions you can work for include:

FAQs on Localization, Mapping, and SLAM using Python Course

  1. Who can take the Localization, Mapping, and SLAM using Python course?

Students and graduates of computer science and related engineering streams can take up Localization, Mapping, and SLAM using Python courses.

  1. Is the Localization, Mapping, and SLAM using Python an online program?

Yes, the SLAM localization course is 100% online.

  1. What is the duration of the Localization, Mapping, and SLAM using Python course?

The advanced localization and mapping course can be completed in 12 weeks.

  1. What is the fee for pursuing the Localization, Mapping, and SLAM using Python course?

The fee for learning SLAM mapping at Skill-Lync is flexible and is available in three plans. The basic plan provides you with two months of access at INR 7000 per month for three months, the Pro plan provides you with four months of access at INR 10,000 per month for three months, and the Premium plan provides you lifetime access at INR 15,000 per month for three months.

  1. How much can a robotic engineer earn?

According to Ambitionbox, the average annual salary of a robotics engineer is INR 3.5 lakhs. However, your pay package may vary with your experience and expertise.

  1. Is there any certificate for completing the Localization, Mapping, and SLAM using Python course?

Yes, you shall be given a course completion certificate after completing the Localization, Mapping, and SLAM using Python course. The top 5% of the scorers will receive a merit certificate alongside the course completion certificate.

  1. Is any technical support available for this SLAM algorithm course?

Yes, you can clear your doubts during coursework from our technical support team through email and forum support.

  1. Can you tell me more about Skill-Lync?

Skill-Lync is among India's leading EdTech platforms dedicated to transforming engineering education. We equip young engineers with the latest skill sets and cutting-edge tools in new-age technologies.

The brainchild of two engineers from Chennai, Skill-Lync, is on a mission to bridge the skill gap between aspiring professionals and the industry's demands through job-oriented courses.

Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.

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1 industry expert

Our instructors are industry experts along with a passion to teach.

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5 years in the experience range

Instructors with 5 years extensive industry experience.

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Areas of expertise

  • Trajectory Optimization
  • RADAR

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