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Postgraduate DiplomaAerospace, UAM, Drones & Space Applications open

Diploma in Line-of-Sight/Feature Perception and Fusion in Denied Environments

Online postgraduate diploma of 800 hours in line-of-sight/feature perception and fusion in denied environments. 8 modules covering GNSS-denied environments, perception sensors, feature extraction, odometry and registration, SLAM, multi-sensor fusion and perception for safe navigation under Implementing Regulation (EU) 2019/947 and ISO 21448 (SOTIF), with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Characterise GNSS-denied environments and their perception challenges.

02

Select and calibrate perception sensors for UAS.

03

Extract and match features robustly.

04

Implement visual, LiDAR and radar odometry.

05

Apply real-time SLAM and mapping techniques.

06

Fuse multiple sensors using factor graphs.

07

Integrate perception into the safe navigation of the vehicle.

Profile

Who it's for

  • Perception engineers.
  • SLAM engineers.
  • Computer vision engineers.
  • Sensor fusion engineers.
  • Autonomous robotics engineers.
  • Researchers in GNSS-free navigation.
  • Engineering or science graduates looking to join drone manufacturers.
Standards & tools

What you'll work with

  • Implementing Regulation (EU) 2019/947 (rules and procedures for the operation of unmanned aircraft)
  • Delegated Regulation (EU) 2019/945 (unmanned aircraft systems and third-country operators)
  • ISO 21448 (SOTIF) (safety of the intended functionality)
  • IEC 60825-1 (laser safety)
  • ASTM F3442 (detect and avoid system performance for small UAS)
  • ISO 13482 (safety requirements for personal care robots)
  • IEEE 1873 (robot map data representation for navigation)
  • ROS 2
  • OpenCV
  • GTSAM
  • Open3D
  • Gazebo
Syllabus

8 modules

  1. 1.1Indoors, tunnels and mining
  2. 1.2Under bridges and in urban canyons
  3. 1.3Forests and dense vegetation
  4. 1.4Low-texture environments
  5. 1.5Smoke, dust and low visibility
  6. 1.6Perception requirements by mission type
  7. 1.7Direct line of sight and occlusions
  8. 1.8Operational safety considerations
  9. 1.9State of the art in autonomous inspection
  10. 1.10Case study: perception requirements for tunnel inspection
  1. 2.1Monocular and stereo cameras
  2. 2.2Event cameras
  3. 2.3Mechanical and solid-state LiDAR
  4. 2.4Millimetre-wave radar
  5. 2.5Thermal cameras
  6. 2.6Time-of-flight sensors
  7. 2.7Intrinsic and extrinsic calibration
  8. 2.8Hardware synchronisation
  9. 2.9Selection by weight and power
  10. 2.10Lab: camera–LiDAR extrinsic calibration
  1. 3.1FAST, ORB and SIFT detectors
  2. 3.2Learned descriptors: SuperPoint
  3. 3.3Matching with SuperGlue and LightGlue
  4. 3.4KLT feature tracking
  5. 3.5Lines and planes as primitives
  6. 3.6Features in point clouds
  7. 3.7Robustness to illumination
  8. 3.8Outlier rejection with RANSAC
  9. 3.9Repeatability evaluation
  10. 3.10Practical exercise: robust matching on real sequences
  1. 4.1Direct and indirect visual odometry
  2. 4.2LiDAR odometry with ICP
  3. 4.3LOAM and its variants
  4. 4.4NDT point cloud registration
  5. 4.5Radar odometry
  6. 4.6Motion estimation with event cameras
  7. 4.7Accumulated errors and drift
  8. 4.8Evaluation with ATE and RPE metrics
  9. 4.9Algorithm comparison
  10. 4.10Lab: LiDAR odometry on a public dataset
  1. 5.1Probabilistic formulation of SLAM
  2. 5.2Factor graphs
  3. 5.3Loop closure
  4. 5.4Visual SLAM: ORB-SLAM3
  5. 5.5LiDAR-inertial SLAM: LIO-SAM and FAST-LIO
  6. 5.6Occupancy maps and OctoMap
  7. 5.7Relocalisation in prior maps
  8. 5.8Multi-session SLAM
  9. 5.9Onboard computing requirements
  10. 5.10Practical exercise: mapping with FAST-LIO in simulation
  1. 6.1Loose and tight fusion
  2. 6.2Factor graphs with GTSAM
  3. 6.3LiDAR, visual and inertial fusion
  4. 6.4Observability and degeneracy
  5. 6.5Uncertainty management
  6. 6.6Sensor fault detection
  7. 6.7Switching between navigation modes
  8. 6.8Integration with the autopilot
  9. 6.9Latency and real time
  10. 6.10Workshop: LiDAR and IMU fusion with GTSAM
  1. 7.1Obstacle detection
  2. 7.2Semantic segmentation
  3. 7.3Cost maps for planning
  4. 7.4Detection of cables and thin structures
  5. 7.5Navigation in narrow corridors
  6. 7.6Autonomous frontier exploration
  7. 7.7Real-time risk assessment
  8. 7.8Validation of perception algorithms
  9. 7.9Reference datasets
  10. 7.10Case study: autonomous exploration of a building
  1. 8.1Definition of the environment and mission
  2. 8.2Sensor selection
  3. 8.3Feature pipeline
  4. 8.4Odometry algorithm
  5. 8.5Fusion strategy
  6. 8.6Integration with planning
  7. 8.7Experimental evaluation
  8. 8.8Analysis of limitations
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within the intake, so the programme fits around your job.

Each module combines theory, guided reading of the original standards and applied exercises. The working software is ROS 2, OpenCV, GTSAM, Open3D and Gazebo, in free, academic or evaluation versions. Exercises are based on real procedures: LiDAR-inertial mapping in GNSS-free environments, evaluation of visual odometry with standard metrics and autonomous indoor exploration. Assessment is continuous by module, plus the final integrative project.

Standards covered: Implementing Regulation (EU) 2019/947, Delegated Regulation (EU) 2019/945, ISO 21448 (SOTIF), IEC 60825-1, ASTM F3442, ISO 13482, IEEE 1873.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Implementing Regulation (EU) 2019/947, Delegated Regulation (EU) 2019/945 and ISO 21448 (SOTIF) applied to real cases, not as a theoretical annex.
Industry software:ROS 2, OpenCV, GTSAM, Open3D and Gazebo, in free, academic or evaluation versions.
Final project:A perception and fusion project for a denied environment, covering sensor selection, feature pipeline, odometry, fusion strategy, integration with planning and experimental evaluation, reviewed by your tutor.
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.

Environment and sensors
Define the environment and mission and select the sensors.
Features and odometry
Build the feature pipeline and the odometry algorithm.
Fusion strategy
Design the fusion strategy and integrate it with planning.
Experimental evaluation
Evaluate the system experimentally and analyse its limitations.
Final project
A perception and fusion project for a denied environment, covering sensor selection, feature pipeline, odometry, fusion strategy, integration with planning and experimental evaluation, reviewed by your tutor.
Career opportunities

Where this takes you

Perception engineerSLAM engineerComputer vision engineerSensor fusion engineerAutonomous robotics engineerGNSS-free navigation researcher
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. A university degree is not required.
  • An 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 800 hours over 6–8 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, OpenCV, GTSAM, Open3D and Gazebo, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme 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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