Last modified: 31 May 2022 13:05
The aim of the course is to give an overview of the key techniques for enabling mobile robots to localise themselves, map their environments or do both simultaneously. The course introduces students to the fundamentals of statistical modelling and state estimation, widely used in automated vehicles and industrial automation.
|Session||First Sub Session||Credit Points||15 credits (7.5 ECTS credits)|
The course is an introduction to the paradigm of probabilistic robotics, applied in the context of mobile robotics. It covers the SLAM problem and its various forms as well as the different variants of recursive Bayesian filtering (e.g., the KF and EKF) and their underlying assumptions. The course begins with a recap of the basic concepts of random variables, probability distributions, conditional independence and Markov assumption. Then, the characteristics of probabilistic motion and sensor models for mobile robots are introduced just as the differences and similarities between Occupancy Grid Maps and Counting. An overview on the characteristics and designs of landmark- and grid-based SLAM solutions is given, including the effects of Rao-Blackwellization. Additionally, the basic python commands to implement Bayes filters and SLAM systems are conveyed.
Information on contact teaching time is available from the course guide.
1x Lab Report: Individual (15%)
1x Take Home Exam (15%)
1x Online Open-Book Exam (70%)
Alternative Resit Arrangements
1x Online Open-Book Test (100%)
There are no assessments for this course.
|Knowledge Level||Thinking Skill||Outcome|
|Procedural||Apply||Apply Bayes (filter) formulae and sample from probability density functions|
|Procedural||Apply||Determine and apply probabilistic sensor and motion models|
|Procedural||Apply||Implement realizations of Bayes filters and compute location estimates for robots|
|Procedural||Analyse||Build and analyse grid maps|
|Procedural||Apply||Determine solutions to data association problems|
|Factual||Analyse||Prove properties of basic concepts from probability theory|
|Procedural||Analyse||Differentiate between localisation and SLAM systems as well as outline auxiliary techniques for SLAM solutions|
|Conceptual||Analyse||Discuss the steps and components of realisations of Bayes filters|
|Procedural||Evaluate||Assess and implement components of landmark- and grid-based solutions to the SLAM problem|