Master’s in Perception in Unstructured Environments Engineering
Online master's programme of 1,900 hours in perception engineering for unstructured environments, with 12 modules covering off-road sensors, calibration, point cloud processing, deep learning, traversability analysis, person and obstacle detection, sensor fusion, data labelling, robustness and embedded deployment, with an assigned technical tutor.
Master’s in Perception in Unstructured Environments Engineering — studying from Canada
Canada's off-road and heavy machinery sector serves mining, forestry, agriculture and construction, all major contributors to the national economy. Manufacturers and dealers in Ontario, Alberta, Saskatchewan and British Columbia supply equipment for resource extraction and infrastructure. Mining activity in Northern Ontario, Quebec and the territories drives demand for rugged vehicle engineering and maintenance expertise. Engineers work on hydraulics, drivetrains, machine safety and special vehicle design.
Fees for Canada: C$12,400 (Canadian dollar), with instalments and employer sponsorship available.
Relevant bodies in Canada
Typical roles
Questions from Canada
Is this programme recognised by professional bodies in Canada for off-road and special vehicle engineering?
SEIUM's qualifications are private, not official state degrees, and are not accredited nationally in Canada. Professional engineering titles are regulated provincially, and vehicle and equipment safety is governed by standards such as those from the CSA and rules from Transport Canada where applicable. Recognition of a private qualification is decided by employers or the relevant body.
What employers in Canada might value this qualification for special vehicles and machinery?
Employers could include mining, forestry, agricultural, construction and specialist vehicle manufacturers across Canada. Each employer decides how much weight to place on a private qualification, and practical experience is often central in these sectors. The final project can demonstrate applied capability, but it does not guarantee any role.
How can I study this while working in Canada, and how is it funded?
The programme is online and in English, so you can study from anywhere in Canada without a student visa. Choose from 300-hour specialist courses, 800-hour postgraduate diplomas or 1,900-hour master's programmes. Fees are payable in instalments, and employer sponsorship is possible, which some Canadian employers offer for job-relevant training.
Take the controls.
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
Identify the perception challenges in unstructured environments.
Select and calibrate sensors for off-road use.
Process point clouds and images with deep learning.
Analyse terrain traversability.
Detect people and obstacles reliably.
Fuse data from multiple sensors.
Deploy robust perception models on embedded hardware.
Who it's for
- Perception engineers developing systems for autonomous off-road machines.
- Computer vision engineers working with deep learning for detection and segmentation.
- Sensor fusion engineers combining LiDAR, radar and camera data.
- Data engineers managing capture, labelling and datasets for autonomy.
- Embedded software engineers deploying models on edge hardware.
- Field robotics researchers working in agriculture, mining or construction.
- Engineering or science graduates looking to enter the agricultural machinery sector.
What you'll work with
- ISO 18497 (safety of autonomous agricultural machines)
- ISO 17757 (safety of autonomous machines in mining)
- ISO 21448 (safety of the intended functionality)
- IEC 60825-1 (safety of laser products)
- Regulation (EU) 2016/679 (data protection)
- Regulation (EU) 2024/1689 (artificial intelligence)
- PyTorch
- Open3D
- ROS 2
- NVIDIA TensorRT
- CVAT
12 modules · 1900 h
1.1Unstructured work environments1.2Absence of lanes and signage1.3Irregular and changing terrain1.4Vegetation and its ambiguity1.5Dust, rain and snow1.6Extreme lighting and backlighting1.7Atypical objects in the environment1.8People in work areas1.9Perception requirements1.10Case study: perception requirements for an autonomous tractor
2.1Mechanical and solid-state LiDAR2.2High-resolution FMCW radar2.3High dynamic range RGB cameras2.4Long-wave thermal cameras2.5Stereo vision for depth2.6Event cameras2.7Sensor performance in dust2.8Selecting the sensor combination2.9Sensor mounting and protection2.10Lab: LiDAR in a dust chamber
3.1Intrinsic camera calibration3.2Extrinsic calibration between sensors3.3Camera-LiDAR calibration3.4Radar calibration3.5Time synchronisation via PTP3.6Online calibration3.7Calibration errors and their effects3.8Calibration tools3.9Calibration maintenance3.10Practical: extrinsic sensor calibration
4.1Filtering and noise removal4.2Ground segmentation4.3Object clustering4.4Point cloud voxelisation4.5Neural networks for point clouds4.6ICP registration4.7Elevation maps4.8PCL and Open3D libraries4.9Computational performance of processing4.10Lab: ground segmentation in a point cloud
5.1Convolutional neural networks5.2Object detection5.3Semantic image segmentation5.4Transformer architectures for vision5.5Model training5.6Training data management5.7Data augmentation5.8Evaluation metrics5.9Model deployment5.10Practical: segmentation of traversable terrain
6.1Terrain type classification6.2Terrain geometry6.3Surface roughness of the terrain6.4Slopes and steps6.5Inference of soil properties6.6Self-supervised learning from driving6.7Cost maps6.8Uncertainty in traversability6.9Validation with field data6.10Case study: traversability cost map
7.1Detecting people wearing work gear7.2Animal detection7.3Negative obstacles such as ditches7.4Vegetation versus rigid obstacles7.5Required detection distances7.6False negative rates7.7ISO 18497 requirements7.8Detection redundancy7.9Tests with mannequins7.10Case study: detection of negative obstacles
8.1Early data fusion8.2Late fusion of detections8.3Bayesian estimation filters8.4Object tracking8.5Occupancy grids8.6Bird's-eye-view representation8.7Uncertainty management8.8Fusion architectures8.9Fusion performance8.10Lab: occupancy grid
9.1Field data capture9.2Managing large volumes9.3Labelling of images and point clouds9.4Labelling tools9.5Labelling quality control9.6Privacy of people captured9.7Synthetic data from simulation9.8Public off-road datasets9.9Data improvement cycle9.10Practical: labelling plan
10.1Effect of dust10.2Effect of rain10.3Effect of fog10.4Sun glare on cameras10.5Night-time operation with artificial lighting10.6Dirt on sensors10.7Sensor self-diagnosis10.8Cleaning systems10.9Controlled degradation of functions10.10Case study: strategy for an obstructed sensor
11.1Embedded graphics processors11.2Edge computing11.3Model optimisation11.4Inference with TensorRT11.5End-to-end latency11.6Energy consumption of computing11.7Hardware robustness11.8Hardware certification11.9Model updates11.10Lab: deployment on a Jetson platform
12.1Scenario definition12.2Sensor selection12.3System calibration12.4Dataset12.5Model development12.6System sensor fusion12.7Robustness evaluation12.8Deployment on embedded hardware12.9Thesis 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 you can combine it with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is PyTorch, Open3D, ROS 2, NVIDIA TensorRT and CVAT, in free, academic or evaluation versions. Testing is studied through real procedures for field data capture, detection tests with mannequins and sensor evaluation in dust. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 18497, ISO 17757, ISO 21448, IEC 60825-1, Regulation (EU) 2016/679, Regulation (EU) 2024/1689.
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
- A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- 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.
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 PyTorch, Open3D, ROS 2, NVIDIA TensorRT and CVAT, 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 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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