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EG504N: LOCALISATION AND MAPPING IN THE INDUSTRIAL DOMAIN (2023-2024)

Last modified: 31 Oct 2023 14:16


Course Overview

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.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Andres San-Millan

What courses & programmes must have been taken before this course?

  • One of Master Of Science In Industrial Robotics or Master of Engineering in Electrical & Electronic Engineering or Master Of Engineering In Electronic & Software Engineering

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

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.

Course Content

  1. Introduction – Nomenclature, history, state of the art, challenges, course logistics
  2. Probability Review – Events, axioms of probability, independent events, Bayes Rule, Bayes Filter
  3. Probabilistic Modelling – Odometry- and velocity-based motion models, beam- and scan-based sensor models
  4. Localisation with Nonparametric Filters – Discrete Bayes Filter, importance sampling, particle filter
  5. Localisation with Gaussian Filters – Kalman Filter, extended Kalman Filter
  6. Mapping with Known Poses – Occupancy maps, reflection probability maps
  7. Landmark-based SLAM – SLAM problem, EKF SLAM, loop closing, Rao-Blackwellization, FastSLAM
  8. Grid-based SLAM – Scan matching, FastSLAM, improved proposals, selective resampling

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

Coursework

Assessment Type Summative Weighting 15
Assessment Weeks 15 Feedback Weeks 16,17,18

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralAnalyseBuild and analyse grid maps
ProceduralApplyImplement realizations of Bayes filters and compute location estimates for robots
ProceduralApplyDetermine and apply probabilistic sensor and motion models
ProceduralEvaluateAssess and implement components of landmark- and grid-based solutions to the SLAM problem

Exam

Assessment Type Summative Weighting 70
Assessment Weeks 19,20 Feedback Weeks 23,24

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualAnalyseDiscuss the steps and components of realisations of Bayes filters
FactualAnalyseProve properties of basic concepts from probability theory
ProceduralAnalyseDifferentiate between localisation and SLAM systems as well as outline auxiliary techniques for SLAM solutions
ProceduralAnalyseBuild and analyse grid maps
ProceduralApplyApply Bayes (filter) formulae and sample from probability density functions
ProceduralApplyDetermine solutions to data association problems
ProceduralApplyDetermine and apply probabilistic sensor and motion models
ProceduralApplyImplement realizations of Bayes filters and compute location estimates for robots
ProceduralEvaluateAssess and implement components of landmark- and grid-based solutions to the SLAM problem

Computer Practical

Assessment Type Summative Weighting 15
Assessment Weeks 8,10,12,17 Feedback Weeks 23,24

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralAnalyseBuild and analyse grid maps
ProceduralApplyImplement realizations of Bayes filters and compute location estimates for robots
ProceduralApplyDetermine and apply probabilistic sensor and motion models
ProceduralApplyApply Bayes (filter) formulae and sample from probability density functions
ProceduralEvaluateAssess and implement components of landmark- and grid-based solutions to the SLAM problem

Formative Assessment

There are no assessments for this course.

Resit Assessments

Resubmission of failed elements

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ProceduralAnalyseBuild and analyse grid maps
ConceptualAnalyseDiscuss the steps and components of realisations of Bayes filters
FactualAnalyseProve properties of basic concepts from probability theory
ProceduralApplyApply Bayes (filter) formulae and sample from probability density functions
ProceduralEvaluateAssess and implement components of landmark- and grid-based solutions to the SLAM problem
ProceduralApplyImplement realizations of Bayes filters and compute location estimates for robots
ProceduralAnalyseDifferentiate between localisation and SLAM systems as well as outline auxiliary techniques for SLAM solutions
ProceduralApplyDetermine and apply probabilistic sensor and motion models
ProceduralApplyDetermine solutions to data association problems

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