Postgraduate Diploma in LiDAR/Vision SLAM and Precision Localisation
Online 800 h Postgraduate Diploma in LiDAR/Vision SLAM and Precision Localisation. 8 modules with ISO 3691-4:2023, IEC 60825-1:2014 and ROS 2. Assigned tutor and open enrolment.
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
Apply state estimation filters in mobile robots.
Calibrate LiDAR, camera and IMU sensors.
Build maps using LiDAR-based SLAM algorithms.
Implement visual and inertial SLAM.
Achieve precision localisation with markers and fusion.
Evaluate algorithms with ATE and RPE metrics.
Deploy localisation systems in industrial AMRs.
Who it's for
- Robotics perception engineer who wants to specialise in LiDAR/Vision SLAM and Precision Localisation.
- SLAM engineer who wants to specialise in LiDAR/Vision SLAM and Precision Localisation.
- Mobile robotics developer who wants to specialise in LiDAR/Vision SLAM and Precision Localisation.
- Computer vision engineer who wants to specialise in LiDAR/Vision SLAM and Precision Localisation.
- AMR integrator who wants to specialise in LiDAR/Vision SLAM and Precision Localisation.
- Researcher in autonomous navigation who wants to specialise in LiDAR/Vision SLAM and Precision Localisation.
- Graduates in engineering or science seeking to enter logistics.
What you'll work with
- ISO 3691-4:2023
- IEC 60825-1:2014
- ISO 8373:2021
- ANSI/RIA R15.08-1-2020
- IEC 61496-3:2018
- ISO 18646-2:2019
8 modules · 800 h
1.1Localisation and mapping problem1.2Pose representations in SE(2) and SE(3)1.3Wheel odometry and its errors1.4Inertial measurement units (IMU)1.5Extended Kalman filter1.6Particle filter1.7Uncertainty and covariance1.8Sensor synchronisation and timestamps1.9Reference frames and TF in ROS 21.10Workshop: odometry and IMU fusion with robot_localization
2.1Mechanical and solid-state 2D and 3D LiDAR2.2Point cloud filtering and downsampling2.3Monocular, stereo and RGB-D cameras2.4Intrinsic camera calibration2.5LiDAR-camera extrinsic calibration2.6Camera-IMU calibration with Kalibr2.7Noise, reflectivity and difficult environments2.8Sensor selection by application2.9Recording datasets with rosbag22.10Practice: LiDAR-camera extrinsic calibration
3.1Point cloud registration with ICP and NDT3.22D scan matching3.3Cartographer and slam_toolbox3.43D SLAM with LOAM and LIO-SAM3.5Pose graphs and optimisation3.6Loop closure detection3.7Occupancy grid maps and point maps3.8Degeneracy in long corridors3.9Drift assessment3.10Laboratory: mapping a warehouse with slam_toolbox
4.1ORB features and descriptors4.2Direct and feature-based visual odometry4.3ORB-SLAM3 architecture4.4VINS-Fusion and visual-inertial odometry4.5Local and global bundle adjustment4.6Visual relocalisation and bags of words4.7Scale in monocular vision4.8Lighting and low-texture environments4.9Visual versus LiDAR comparison4.10Practice: ORB-SLAM3 on a custom dataset
5.1Map-based localisation with AMCL5.2Scan matching localisation5.3AprilTag fiducial markers5.4Reflectors and beacons5.5UWB for indoor localisation5.6High-precision multisensor fusion5.7Millimetre precision at workstations5.8Robustness to environmental changes5.9Accuracy and repeatability metrics5.10Laboratory: localisation with AprilTag and LiDAR
6.1KITTI, EuRoC and TUM datasets6.2Absolute trajectory error (ATE)6.3Relative pose error (RPE)6.4evo evaluation tool6.5Ground truth with motion capture6.6Long-duration localisation tests6.7Robustness in dynamic environments6.8Computational cost and real-time performance6.9Evaluation reports6.10Case study: comparison of three SLAM algorithms
7.1Embedded hardware: Jetson and x867.2Compute and GPU optimisation7.3Large-scale and multi-session maps7.4Updating maps during operation7.5Fleet map management7.6Integration with Nav27.7Functional safety and localisation7.8Diagnosing localisation loss7.9Sensor maintenance7.10Practice: deployment on a real AMR
8.1Environment definition and requirements8.2Sensor selection and calibration8.3Map building8.4Localisation in operation8.5Accuracy improvement at critical points8.6Evaluation against ground truth8.7Embedded deployment on the robot8.8Technical localisation report8.9Dissertation and presentation8.10Final review with the tutor
How you'll learn
Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, making it possible to combine with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is ROS 2, slam_toolbox, ORB-SLAM3, Kalibr, evo, in free, academic or evaluation versions. Tests are studied using real map-building procedures in industrial warehouses, drift assessment with ground truth, and precision localisation at workstations. Assessment is continuous by module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Apply state estimation filters in mobile robots.
Calibrate LiDAR, camera and IMU sensors.
Build maps using LiDAR-based SLAM algorithms.
Implement visual and inertial SLAM.
Achieve precision localisation with markers and fusion.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory texts.
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 consists of 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
The work uses ROS 2, slam_toolbox, ORB-SLAM3, Kalibr and evo. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Please check the credit transfer policy when you enrol.
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