Postgraduate Diploma in Dense LiDAR and 3D SLAM for Automotive
Online 800-hour Postgraduate Diploma in Dense LiDAR and 3D SLAM for Automotive. 8 modules covering IEC 60825-1:2014, ISO 26262:2018 and Open3D. 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
Compare LiDAR technologies and their performance for automotive applications.
Process dense point clouds efficiently.
Implement LiDAR registration and odometry algorithms.
Build 3D SLAM systems with graph optimisation.
Generate dense maps and environment representations.
Apply 3D perception networks to LiDAR data.
Integrate LiDAR-based localisation into the driving stack.
Who it's for
- SLAM engineer looking to specialise in Dense LiDAR and 3D SLAM for Automotive.
- LiDAR perception engineer looking to specialise in Dense LiDAR and 3D SLAM for Automotive.
- Localisation engineer looking to specialise in Dense LiDAR and 3D SLAM for Automotive.
- Robotics engineer looking to specialise in Dense LiDAR and 3D SLAM for Automotive.
- 3D mapping specialist looking to specialise in Dense LiDAR and 3D SLAM for Automotive.
- 3D vision researcher looking to specialise in Dense LiDAR and 3D SLAM for Automotive.
- Graduates in engineering or science seeking to enter the automotive sector.
What you'll work with
- IEC 60825-1:2014
- ISO 26262:2018
- ISO 21448:2022 (SOTIF)
- ASPRS 1.4
- ISO 34503:2023
- UN Regulation No. 157
- ISO 16750-3:2023
8 modules · 800 h
1.1Time-of-flight principle1.2Mechanical, MEMS and flash LiDAR1.3Coherent FMCW LiDAR1.4Wavelengths of 905 and 1550 nm1.5Resolution, range and field of view1.6Laser safety and classes1.7Noise, reflectivity and intensity1.8Selection criteria for automotive use1.9LiDAR interference and its mitigation1.10Case study: comparison of commercial sensors
2.1KD-Tree and Octree Structures2.2Voxel filtering and downsampling2.3GROUND REMOVAL2.4Motion Distortion Correction2.5Normal estimation and descriptors2.6Segmentation and Clustering2.7PCD, and ROS Message Formats2.8Visualization of large point clouds2.9Range image projection2.10Practice: Pre-processing pipeline with open3d
3.1Point-to-point and point-to-plane ICP3.2NDT registration3.3Feature-based registration3.4LOAM odometry3.5LeGO-LOAM and F-LOAM variants3.6KISS-ICP and minimalist approaches3.7Drift estimation3.8Evaluation with KITTI odometry3.9Global registration with FPFH and RANSAC3.10Laboratory: LiDAR odometry on KITTI
4.1Probabilistic formulation of SLAM4.2Pose graphs and factors4.3Loop closure detection4.4Scan Context descriptors4.5Optimisation with g2o and GTSAM4.6LIO-SAM with inertial fusion4.7FAST-LIO and iterated filters4.8Geometrically degenerate environments4.9Multi-session SLAM and map merging4.10Laboratory: SLAM with LIO-SAM in ROS 2
5.1Voxel maps and OctoMap5.2Implicit surfaces and TSDF5.3Meshes and surface reconstruction5.4Elevation maps5.5Implicit neural representations5.6Map compression and streaming5.7Dynamic objects in the map5.8Colour and intensity fusion5.9Map georeferencing5.10Practical: dense reconstruction of a street
6.13D detection with PointPillars6.2Voxel-based networks6.3Semantic segmentation with RangeNet++6.43D object tracking6.5Free-space detection6.6nuScenes and Waymo Open datasets6.7mAP and NDS metrics6.8Efficient deployment on embedded GPUs6.93D occupancy and scene flow prediction6.10Laboratory: 3D detection with OpenPCDet
7.1Localisation on a prior map7.2Fusion with GNSS and inertial data7.3Relocalisation and cold start7.4Map updates with changes7.5Latency and accuracy requirements7.6LiDAR-IMU calibration7.7Evaluation with ATE and RPE7.8Integration into the driving stack7.9LiDAR localisation integrity7.10Case study: localisation on an urban circuit
8.1Selection of dataset or vehicle8.2Point cloud pre-processing8.3Odometry and inertial fusion8.4Loop closure and optimisation8.5Resulting dense map8.6Quantitative accuracy assessment8.7Failure and degeneracy analysis8.8Pipeline documentation8.9Report and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and 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 Open3D, PCL, ROS 2, GTSAM, OpenPCDet, in free, academic or evaluation versions. Tests are studied on real LiDAR odometry procedures over KITTI sequences, 3D SLAM with inertial fusion on an instrumented vehicle, and 3D object detection on nuScenes. Assessment is continuous per 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.
Compare LiDAR technologies and their performance for automotive applications.
Process dense point clouds efficiently.
Implement LiDAR registration and odometry algorithms.
Build 3D SLAM systems with graph optimisation.
Generate dense maps and environment representations.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of 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 total hours spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The programme works with Open3D, PCL, ROS 2, GTSAM, OpenPCDet. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing 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 are credited towards the advanced programme. Check the credit transfer policy when you enrol.
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