Master’s in Resilient Autonomy and Navigation in GPS-Denied Environments Engineering
Online master's programme of 1,900 hours in resilient autonomy and navigation engineering for GPS-denied environments. 12 modules covering ISO 26262, ISO 21448 (SOTIF), IEEE 1952, inertial navigation, sensor fusion, visual and LiDAR SLAM, terrain-referenced navigation, perception, planning and validation, using ROS 2, GTSAM and ORB-SLAM3, with an assigned technical tutor.
Master’s in Resilient Autonomy and Navigation in GPS-Denied Environments Engineering — studying from South Africa
South Africa has a established defence industry centred on companies such as Denel and Paramount Group, with capabilities in land systems, armoured vehicles and munitions. The sector supports engineering roles in systems integration, vehicle design and test and evaluation. Defence procurement and export controls are overseen by government bodies, and the industry works closely with the South African National Defence Force. Engineers in this field typically combine mechanical, electrical and systems skills.
Fees for South Africa: R 185 000 (South African rand), with instalments and employer sponsorship available.
Relevant bodies in South Africa
Typical roles
Questions from South Africa
Which South African bodies regulate or recognise defence and military engineering professionals?
Engineers in South Africa are registered through ECSA under the Engineering Profession Act, and defence programmes are guided by institutions such as the South African National Defence Force and Armscor, which set their own requirements. SEIUM is a private online institute, and its qualifications are not state degrees or accredited by a national body. Recognition is decided by these bodies and your employer, so confirm with them first.
Would this qualification help me find work in South Africa's defence or land systems sector?
South Africa has a defence industry with state-linked entities and private contractors, and employers usually require security clearances, relevant experience and recognised qualifications. A private online programme may support your application but does not guarantee employment or clearance. Check requirements with prospective employers, as roles in this sector can be sensitive and tightly controlled.
Can I study alongside a job, and are there funding options?
The course is 100% online and taught in English, so you need strong English, and no student visa is needed to study from home. Master's programmes are 1,900 hours, postgraduate diplomas 800 hours and specialist courses 300 hours, each with an assigned technical tutor. Fees are payable in instalments, and employer sponsorship is possible, so ask whether your employer would fund part of the training.
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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Analyse the vulnerabilities of satellite positioning in degraded environments.
Design inertial navigation and multi-sensor fusion solutions.
Implement visual and LiDAR-based SLAM algorithms.
Develop environment-referenced navigation systems.
Apply robust planning techniques under uncertainty.
Assess the functional safety of autonomous systems.
Validate autonomy through simulation and field testing.
Who it's for
- Navigation and localisation engineers working without reliable GNSS.
- Perception engineers developing detection and scene understanding for robots and vehicles.
- Sensor fusion engineers integrating inertial, visual, LiDAR and radar data.
- Autonomous robotics researchers focusing on SLAM and resilient estimation.
- Autonomy validation engineers designing simulation and field test campaigns.
- Autonomous systems architects responsible for fault-tolerant designs.
- Engineering or science graduates aiming to enter industrial and logistics robotics.
What you'll work with
- ISO 26262 (functional safety of road vehicles)
- ISO 21448 (safety of the intended functionality, SOTIF)
- IEEE 1952 (inertial sensors)
- IEEE 952 (fibre-optic gyroscopes)
- UL 4600 (safety evaluation of autonomous products)
- RTCA DO-316 (minimum operational performance standards for GPS/aircraft-based augmentation)
- Regulation (EU) 2019/947 (UAS operations)
- ROS 2
- GTSAM
- ORB-SLAM3
- CARLA
- MATLAB Navigation Toolbox
12 modules · 1900 h
1.1GNSS vulnerabilities: interference and multipath1.2Urban, underground and indoor scenarios1.3Alternative and complementary PNT concept1.4Integrity, continuity and availability metrics1.5Resilient autonomy: definition and levels1.6Analysis of navigation requirements by mission1.7Centralised and distributed architectures1.8Error models of navigation sensors1.9Regulatory framework for PNT resilience1.10Case study: analysis of GNSS degradation in a tunnel
2.1Mechanisation equations in the NED and ECEF frames2.2MEMS, FOG and RLG sensors: performance2.3Initial alignment and gyrocompassing2.4Modelling of biases and random walk2.5Allan variance for sensor characterisation2.6Error propagation in pure inertial navigation2.7Zero-velocity updates (ZUPT)2.8Pedestrian and odometry-based navigation2.9Laboratory and field calibration2.10Lab: Allan analysis with real IMU data
3.1Extended and unscented Kalman filters3.2Particle filters for non-linear navigation3.3Loosely, tightly and deeply coupled fusion3.4Smoothing and factor graph optimisation3.5Sensor fault detection and exclusion3.6Observability and conditioning3.7Time synchronisation of sensors3.8Multimodal fusion with uncertainty3.9Estimator consistency: NEES and NIS3.10Practical exercise: INS/odometry fusion with GTSAM
4.1Camera models and intrinsic calibration4.2Monocular and stereo visual odometry4.3Visual-inertial odometry (VIO)4.4Feature-based SLAM: ORB-SLAM34.5Direct and semi-direct SLAM4.6Loop closure and relocalisation4.7Dense maps and volumetric representation4.8Robustness to lighting and poor texture4.9Evaluation with the EuRoC and KITTI datasets4.10Lab: VIO with VINS-Fusion in ROS 2
5.1Principles of mechanical and solid-state LiDAR5.2Point cloud registration: ICP and NDT5.3LiDAR odometry and mapping (LOAM)5.4FMCW radar for odometry in adverse conditions5.5LiDAR-inertial fusion5.6Scene segmentation and feature extraction5.7High-definition maps5.8Geometric degeneracy in corridors and tunnels5.9Computational cost and on-board processing5.10Practical exercise: LiDAR odometry with LIO-SAM
6.1Terrain correlation: TERCOM and TAN6.2Magnetic anomaly navigation6.3Gravimetry-based navigation6.4Signals of opportunity: LTE, Wi-Fi and TV6.5Automated celestial navigation6.6Image-matching navigation6.7Reference databases and their quality6.8Map uncertainty and its propagation6.9Integration with hybrid filters6.10Case study: localisation with signals of opportunity
7.1Object detection with convolutional networks7.2Semantic segmentation for navigation7.3Monocular depth estimation7.4Multi-object tracking7.5Agent trajectory prediction7.6Robust perception against out-of-distribution data7.7Uncertainty quantification in deep learning7.8Datasets and annotation7.9Efficient deployment on embedded hardware7.10Workshop: obstacle detector with YOLO on Jetson
8.1Global and local planning8.2Graph search and random sampling8.3Planning under uncertainty: POMDP8.4Behaviour architectures and decision trees8.5Mission management and replanning8.6Autonomous exploration of unknown environments8.7Localisation-aware planning8.8Dynamic and energy constraints8.9Formal verification of behaviours8.10Practical exercise: planner with Nav2 and behaviour trees
9.1Fault-tolerant architectures9.2Run-time integrity monitoring9.3Sensor anomaly detection9.4Sensor cybersecurity and spoofing9.5Degraded modes and graceful degradation9.6Functional safety ISO 26262 and SOTIF ISO 214489.7STPA analysis of autonomous systems9.8Safety arguments with GSN9.9Scenario-based testing9.10Case study: STPA analysis of an autonomous robot
10.1Photorealistic simulation with CARLA and AirSim10.2Generation of scenarios and edge cases10.3HIL and SIL test benches10.4Field testing methodology10.5Navigation performance metrics10.6Reference datasets and ground truth10.7Testing in DARPA SubT-type underground environments10.8Statistical analysis of results10.9Test documentation and traceability10.10Lab: VIO validation in a simulator
11.1Mining and underground robotics11.2Autonomous vehicles in warehouses and indoor spaces11.3UAV navigation in infrastructure inspection11.4Maritime and underwater navigation11.5PNT resilience regulations in Europe11.6Ethics and responsibility in autonomous systems11.7Certification requirements by domain11.8Management of autonomy projects11.9Technology transfer and open standards11.10Workshop: certification roadmap for a robot
12.1Definition of the navigation case12.2Literature and state-of-the-art review12.3Sensor selection and architecture12.4Estimator implementation12.5Integration on a platform or simulator12.6Test protocol design12.7Analysis of results and robustness12.8Conclusions and future work12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training 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 the intake, so the programme can be combined with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is ROS 2, GTSAM, ORB-SLAM3, CARLA and MATLAB Navigation Toolbox, in free, academic or evaluation versions. Practical work is based on real procedures for validating navigation in photorealistic simulation, testing in underground and indoor environments, and bench characterisation of inertial sensors. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 26262, ISO 21448 (SOTIF), IEEE 1952, IEEE 952, UL 4600, RTCA DO-316, Regulation (EU) 2019/947.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable 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.
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, GTSAM, ORB-SLAM3, CARLA and MATLAB Navigation Toolbox, 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 suitable computer 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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