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Master'sRobotics, Automation & Control Applications open

Master’s in Navigation and SLAM for AMR Engineering

Online master's programme of 1,900 hours in navigation and SLAM engineering for AMRs. 12 modules covering kinematics and sensors, probabilistic state estimation, LiDAR and visual SLAM, pose-graph optimisation, path planning, local control, industrial localisation, safety and validation under ISO 3691-4:2023, ISO 13849-1:2023 and IEC 61496-3:2018, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Model the kinematics and sensors of autonomous mobile robots.

02

Apply Bayesian filters to AMR localisation.

03

Build maps using LiDAR and visual SLAM techniques.

04

Optimise pose graphs with loop closures.

05

Configure global planners and local controllers in Nav2.

06

Integrate ISO 3691-4 requirements into navigation.

07

Validate navigation performance with metrics and simulation.

Profile

Who it's for

  • Robotic navigation engineers.
  • ROS 2 software developers.
  • SLAM specialists.
  • Autonomous mobile robot integrators.
  • Perception engineers.
  • AMR deployment managers.
  • Engineering or science graduates aiming to enter intralogistics.
Standards & tools

What you'll work with

  • ISO 3691-4:2023 (safety of driverless industrial trucks)
  • ISO 13849-1:2023 (safety-related parts of control systems)
  • IEC 61496-3:2018 (active opto-electronic protective devices – scanners)
  • ISO 8373:2021 (robotics vocabulary)
  • ANSI/RIA R15.08-1-2020 (safety of industrial mobile robots)
  • VDA 5050 (AGV/AMR–fleet manager communication interface)
  • Directive 2006/42/EC (EU Machinery Directive)
  • Regulation (EU) 2023/1230 (EU Machinery Regulation)
  • ROS 2 Nav2
  • SLAM Toolbox
  • Google Cartographer
  • GTSAM
  • NVIDIA Isaac Sim
Syllabus

12 modules · 1900 h

  1. 1.1Differential and omnidirectional kinematic models
  2. 1.2Mecanum and Ackermann platforms in intralogistics
  3. 1.3Wheel odometry and accumulated drift
  4. 1.4Proprioceptive sensors: encoders and IMU
  5. 1.52D and 3D LiDAR for mobile robots
  6. 1.6Stereo, RGB-D and ToF cameras
  7. 1.7Time synchronisation and extrinsic calibration
  8. 1.8Reference frames and TF2 transforms
  9. 1.9ROS 2 architecture for mobile robots
  10. 1.10Lab: odometry calibration with UMBmark
  1. 2.1Bayes' theorem and recursive filters
  2. 2.2Extended Kalman filter for localisation
  3. 2.3Unscented Kalman filter and sigma points
  4. 2.4Particle filters and resampling
  5. 2.5Adaptive Monte Carlo localisation (AMCL)
  6. 2.6Sensor fusion with robot_localization
  7. 2.7Motion and observation models
  8. 2.8The kidnapped robot problem
  9. 2.9Consistency evaluation with NEES
  10. 2.10Practical: fusing IMU and odometry with an EKF
  1. 3.1Formulation of the full SLAM problem
  2. 3.2Point cloud registration with ICP and NDT
  3. 3.3Real-time correlative scan matching
  4. 3.4Cartographer: submaps and loop closure
  5. 3.5SLAM Toolbox for serial mapping
  6. 3.6LOAM, LeGO-LOAM and LIO-SAM
  7. 3.7Probabilistic occupancy grids
  8. 3.83D maps with OctoMap and voxels
  9. 3.9Mapping large-area facilities
  10. 3.10Lab: warehouse map with SLAM Toolbox
  1. 4.1Multi-view geometry and the pinhole model
  2. 4.2ORB and SIFT features
  3. 4.3ORB-SLAM3 in monocular and stereo mode
  4. 4.4Direct visual odometry: DSO and LSD-SLAM
  5. 4.5IMU preintegration and VINS-Mono
  6. 4.6RTAB-Map with RGB-D cameras
  7. 4.7Bag of visual words for place recognition
  8. 4.8Robustness to lighting and dynamic environments
  9. 4.9AprilTag markers for relocalisation
  10. 4.10Practical: visual-inertial odometry with the EuRoC dataset
  1. 5.1Factor graphs and least squares
  2. 5.2Sparse Gauss-Newton and Levenberg-Marquardt
  3. 5.3g2o, GTSAM and Ceres Solver libraries
  4. 5.4Incremental optimisation with iSAM2
  5. 5.5Loop closure detection and validation
  6. 5.6Robust cost functions against outliers
  7. 5.7Marginalisation and sliding windows
  8. 5.8Memory management in persistent maps
  9. 5.9Multi-session SLAM and map merging
  10. 5.10Workshop: pose graph with GTSAM
  1. 6.1Configuration space and costmaps
  2. 6.2A*, Dijkstra and Theta* algorithms
  3. 6.3Sampling-based planners RRT and RRT*
  4. 6.4State lattice with motion primitives
  5. 6.5Smac Planner in Nav2
  6. 6.6Narrow aisles between racks
  7. 6.7Non-holonomic constraints and Dubins curves
  8. 6.8No-go zones with keepout filters
  9. 6.9Replanning with changing maps
  10. 6.10Practical: comparison of planners in Nav2
  1. 7.1Dynamic Window Approach and variants
  2. 7.2Timed Elastic Band for smooth trajectories
  3. 7.3Regulated Pure Pursuit for path tracking
  4. 7.4MPPI: sampling-based predictive control
  5. 7.5Velocity obstacles and navigation among people
  6. 7.6Tracking dynamic obstacles
  7. 7.7Costmap layers: inflation and voxel layer
  8. 7.8Behaviour trees and recoveries in Nav2
  9. 7.9Tuning velocity and acceleration profiles
  10. 7.10Lab: aisle navigation with pedestrians
  1. 8.1Localisation with reflectors and beacons
  2. 8.2Natural navigation versus magnetic tape
  3. 8.3UWB indoor positioning
  4. 8.4Semantic maps and operating zones
  5. 8.5Layout changes and map updates
  6. 8.6Precise docking to charging stations
  7. 8.7Docking with markers and point clouds
  8. 8.8Accuracy in transfers to conveyors
  9. 8.9Localisation integrity and confidence
  10. 8.10Case study: docking at a transfer station
  1. 9.1Safety laser scanners and protective fields
  2. 9.2Speed-dependent field switching
  3. 9.3ISO 3691-4 requirements for driverless vehicles
  4. 9.4Safety functions to ISO 13849-1
  5. 9.5Negative obstacles, ramps and level changes
  6. 9.6Semantic segmentation with lightweight networks
  7. 9.7Person detection with LiDAR and vision
  8. 9.8Coexistence with forklifts and operators
  9. 9.9Validation of braking distances
  10. 9.10Practical: designing safety fields
  1. 10.1Simulation in Gazebo with ROS 2
  2. 10.2NVIDIA Isaac Sim for logistics environments
  3. 10.3Synthetic scenarios and datasets
  4. 10.4ATE and RPE trajectory metrics
  5. 10.5KITTI, TUM RGB-D and EuRoC benchmarks
  6. 10.6Recording with rosbag2 and reproducibility
  7. 10.7Hardware-in-the-loop system testing
  8. 10.8Analysis of localisation failures
  9. 10.9Field acceptance criteria
  10. 10.10Workshop: trajectory evaluation with evo
  1. 11.1Embedded computing on Jetson and IPC
  2. 11.2Latency and power optimisation
  3. 11.3Integration with WMS and fleet managers
  4. 11.4VDA 5050 interface between AMR and master control
  5. 11.5Maps shared between several robots
  6. 11.6Monitoring and diagnostics in operation
  7. 11.7OTA updates and version control
  8. 11.8Cybersecurity of robotic software
  9. 11.9Map maintenance in continuous service
  10. 11.10Case study: commissioning at a logistics centre
  1. 12.1Analysis of the environment and navigation requirements
  2. 12.2Selection of sensors and on-board computing
  3. 12.3Implementation of the SLAM pipeline
  4. 12.4Configuration of the Nav2 stack
  5. 12.5System validation in simulation
  6. 12.6Tests with a real robot or dataset
  7. 12.7Accuracy and robustness analysis
  8. 12.8Conclusions and future work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

A 100% online programme on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it fits alongside your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is ROS 2 Nav2, SLAM Toolbox, Google Cartographer, GTSAM and NVIDIA Isaac Sim, in free, academic or evaluation versions. Exercises are based on real procedures: mapping logistics facilities with LiDAR, evaluating localisation with public datasets, and tuning navigation in simulation. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 3691-4:2023, ISO 13849-1:2023, IEC 61496-3:2018, ISO 8373:2021, ANSI/RIA R15.08-1-2020, VDA 5050, Directive 2006/42/EC, Regulation (EU) 2023/1230.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 3691-4:2023, ISO 13849-1:2023 and IEC 61496-3:2018 applied to real cases, not as a theoretical annex.
Industry software:ROS 2 Nav2, SLAM Toolbox, Google Cartographer, GTSAM and NVIDIA Isaac Sim, in free, academic or evaluation versions.
Master's Thesis with defence:A complete AMR navigation system, from sensor selection and the SLAM pipeline to Nav2 configuration and accuracy validation, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Analyse the environment
Define the navigation requirements and select sensors and on-board computing.
Implement the SLAM pipeline
Build and tune the SLAM pipeline with loop closure and pose-graph optimisation.
Configure Nav2
Set up global planning, local control and recovery behaviours in Nav2.
Validate accuracy
Validate the system in simulation and with a real robot or dataset using ATE and RPE metrics.
Master's Thesis
Present the navigation system and its accuracy and robustness analysis in your Master's Thesis, defended before a panel.
Career opportunities

Where this takes you

Robotic Navigation EngineerROS 2 Software DeveloperSLAM SpecialistAutonomous Mobile Robot IntegratorPerception EngineerAMR Deployment Manager
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent proven professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with ROS 2 Nav2, SLAM Toolbox, Google Cartographer, GTSAM and NVIDIA Isaac Sim, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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