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Localization, Mapping and SLAM using Python

Localization, Mapping and SLAM

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A Quick Overview

Do you sometimes travel in your car and are suddenly in an unknown location where the gps also fails? Now imagine the same scenario occurring in an Autonomous Vehicle, where you are just suddenly lost and because the map can’t locate you, you are in the middle of nowhere. 

To avoid such a night mare, Localisation, Mapping and SLAM is performed. SLAM uses algorithms that makes it possible to locate the users and simultaneously update the map. The algorithms work in a manner that makes it possible to locate a user whether they are under a tunnel or in a completely isolated area. 

The course on Localisation, Mapping and SLAM focuses on teaching students the algorithms that can be implemented in a robot to help them locate the passenger as well as update the map simultaneously. The course is divided into a 12 week program, where each week the students learn how the process of SLAM can be more efficient. The knowledge of this course can be applied in robotics, autonomous vehicles, drones etc. 

Each week the student will have two practical sessions and a challenge at the end of the week, to put in practical use what they have learned in theory. Other than this, the student will have two projects that they will be asked to work on. These projects help the student get a better understanding on the subject. 

To help you know more about the course, a detailed syllabus is given below. 


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In order to track the movement of a robot in an unknown environment, continuously updating its location while it's making a movement is needed. This tracing of the path also helps the robot in taking a reference from this map, in the future. 

In the first week, we will look into what  Localization, Mapping, and SLAM actually are and how they are applied in real life. The topics that will be covered are:

  •  Introduction about the course 
  •  Introduction about State Estimation & Localization 
  •  Real-life examples of Localization 
  •  Introduction about Mapping  
  •  Examples 
  •  SLAM – Introduction, and examples

2Kalman Filters

Very often when a car passes through a tunnel or location where there is a lot of disturbance, determining their exact location becomes difficult. These disturbances are called noise, and one needs to overcome this noise to get the continuous location of the car. To do this, Kalman filters are used. 

Under Kalman filters in this week, we will be covering 

  • MLE and MAP 
  • Bayes Inference 
  • Gaussian Distribution 
  • Kalman filter

3Extended Kalman Filters, UKF

Kalman filters are further divided into Extended Kalman Filters and Unscented Kalman Filters. While EKF uses a few points to estimate the location, UKF uses a number of points to estimate a given location. How is one better than the other and why is it needed will be explained in detail in week 3. 

The topics that will be covered in this week are:

  • Introduction to Nonlinear Kalman Filters 
  • EKF 
  • Solved example of EKF 
  • UKF

4Particle Filter

Another type of filter that is used in the estimation of an object. The particle filter is one of the most widely used filters after Kalman Filters. 

Under particle filters the topic that will be covered are:

  • Introduction to particle filter 
  • Examples of a particle filter 
  • Comparison between Gaussian and Nonparametric filters

5Monte Carlo Localization

Using the range sensor, odometer, and a map of the location, Monte Carlo Localization helps in estimating the position and orientation of the object of interest. 

In this week, the topics that will be covered are:

  • Explaining MCL with example 
  • Properties of MCL 
  • Discussion problem solving using MCL

6GNSS/INS Sensing for Pose Estimation

An aircraft, before landing needs to be sure of its position, a self-driving car also needs to be alert of its path. While driving, the car should be on the road and also alert of its environment. This is done by using sensors such as GNSS/INS. 

In this week, we will learn about:

  • Sensor Fusion and the need for it. 
  • Introduction for Pose Estimation using GNSS and  INS 
  • Why and where do we need sensor fusion 4.
  • Demonstration

7Camera and Lidar data fusion

Camera and Lidar (or Light Detection and Ranging) use two different approaches in order to detect an object. By fusing these two parameters, the distance at which an object is from the self-driving car can be estimated with accuracy. 

In this week, we will cover:

  • Camera and LiDAR parameters 
  • Applications 
  • Demonstration

8Introduction to SLAM/ Mapping

During the course of this week, you will be learning about SLAM/Mapping.  Simultaneous Localization And Mapping(SLAM) is a computational problem that constructs or updates a map of an unknown environment and also keeps a track of the robot’s location.

In this week we will cover, 

  • Introduction to Mapping and SLAM 
  • Real – Life examples 
  • Features like ORB

9 Occupancy Grid Mapping

During the course of this week, you will be learning about Occupancy Grid Mapping. Occupancy Grid Mapping refers to a family of computer algorithms that address the problem of generating maps from noisy and uncertain sensor measurement data for mobile robots.

In this week we will cover, 

  • Algorithm 
  • Demonstration 
  • Examples


During the course of this week, you will be learning about EKF SLAM. EKF SLAM algorithms are used for maximum feasible algorithm for data association. It is basically a class of algorithm that utilizes the Extended Kalman Filter (EKF) for Simultaneous Localization And Mapping (SLAM). 

In this week we will cover,

  • Comparison with EKF localization 
  • Application 
  • Advantages and Disadvantages


FAST SLAM is a method to detect the position of an object as it travels a distance by mapping all the landmarks it encounters in the place. If there are N number of landmarks in a particular space, then the fast slam approach will take note of all these landmarks to give the desired result. 

In this week, we will be learning about:

  • Application  
  • Advantages and Disadvantages


One way of detecting the position of the self-driving car or robot is to take an estimation of its movement and the distance it has from one landmark. This gives the idea of where the object is most likely to be next, making it easier to place its location on the map. 

In the last week of this course, the students will learn about :

  • Graph SLAM 
  • Application 
  • Advantages and Disadvantages 

Projects Overview


  • Graduates with a bachelors in ECE, EEE or Mechanical



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Frequently Asked Questions

1Who can take your course?

This course is for students and professionals who want to get started with Localization, Mapping and SLAM and gain with practical knowledge of implementing several of these algorithms. For this course it is assumed that the student has basic knowledge in Python and MATLAB, and can write and run the scripts using them, also they can understand linear algebra, statistics and probability.

2What is included in your course?

This course will explain the fundamentals of state estimation using gaussian and nonparametric filters, localization by a popular global and local localization algorithm – Monte Carlo Localization. It also introduces mapping with occupancy grid mapping, further it extends SLAM and different SLAM algorithms.

3What will the student gain from your course?

Students will gain practical knowledge of implementing algorithms involved in Localization, Mapping and SLAM.

4What software skills are you teaching and how well are these tools used in the industry?

The software skills will include python and MATLAB.

5What is the real world application for the tools and techniques will you teach in this course?

The real-world application includes robotics and autonomous systems, such as drones, self-driving cars etc.

6Which companies use these techniques and for what?

All the organization and research institutes involved in developing robotics and autonomous systems.

7How is your course going to help me in my path to MS or PhD?

The topics described in course are advanced and will help the student for his masters, while for PhD it can be a foundation to start new research in this field.

8How is this course going to help me get a job?

Various companies are involved in developing autonomous systems with applications in various fields, and this course contains lot of practical aspects on which one can get the job.


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  • Top 5% of the class will get a merit certificate
  • Course completion certificates will be provided to all students
  • Build a professional portfolio
  • Automatically link your technical projects
  • E-verified profile that can be shared on LinkedIn


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