Simultaneous Localization and Mapping (SLAM)

Jacky Baltes

National Taiwan Normal University (NTNU)

Taipei, Taiwan

jacky.baltes@ntnu.edu.tw

Important Updates

• Mon 14 Sep 2020 16:39:54 PM CST. Page opened.

• Join the http://www.discord.com server NTNU https://discord.gg/nWPHsUU. Send a message !register reinforcement-learning to the @NTNU Online Lecturing Support bot

Content

This course covers a fundamental problem in robot navigation - how can a robot use sensor feedback of the environment to create a local map around it and how can a robot localize itself within t hat map.

Assessment

Your grade in the course depends on the following assessments:

1. Assignment 1: 20%

2. Midterm Test: 20%

3. Assignment 2: 20%

4. Final Exam: 40%

Lecture Slides

The course uses a combination of traditional lecture slides and interactive jupyter notebooks. Some of the notebooks are hosted on the Google Colaboratory and Microsoft Azure Notebooks servers.

The following lecture slides are available:

1. Introduction to Python Programming

2. Basic Probability

3. Kalman Filter

4. Gaussian Distribution and Expectation Maximization

5. Motion Model Exercise

6. Sensor Model

7. Particle Filters

8. Sift and Surf Features

9. Camera Calibration

   1. Linear Distance Transform

   2. Moore Penrose Pseudo Inverse Method

Assignments

Sample Code

Microsoft Azure Notebooks

Google Colaboratory

Link to Jupyter Notebooks