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Masters · 1600 h · 11 modules
Master's in Sensors and Autonomous Navigation Systems
Prepares you to lead the future mobility revolution. Master key technologies in environmental perception, sensor fusion, localization and mapping, and planning and control for autonomous land, air, and sea vehicles. This program…
Duration1600 h
FormatOnline
Starts28 December
Places20 / 20
Prior knowledge
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Programme overview
What is the Master’s in Sensors and Autonomous Navigation Systems?
The Master in Sensors and Autonomous Navigation Systems
Prepares you to lead the future mobility revolution. Master key technologies in environmental perception, sensor fusion, localization and mapping, and planning and control for autonomous land, air, and sea vehicles. This program provides comprehensive training with practical training in simulators and real platforms, applied research projects, and a focus on the latest trends in Artificial Intelligence and Robotics.
Differentiating Advantages
- Multidisciplinary Training: Integrates knowledge of electronics, computer science, mathematics, and control systems.
- Specialized Laboratories: Access to state-of-the-art equipment for experimentation and development.
- Real-World Projects: Collaboration with leading companies in the sector to solve specific challenges.
- Expert Professors: Teaching staff with extensive experience in research and development in autonomous navigation.
- Career Opportunities: Wide range of opportunities in automotive, aerospace, logistics, and robotics.
Programme type
Master's Degree
Study mode
Online
Duration
1,600 hours
Modules
11
Start date
07-12-2026
Programme highlights
- Cutting-edge technology: Master the most advanced sensors and state-of-the-art autonomous navigation techniques.
- Real-world applications: Learn to design and implement systems in robotics, autonomous vehicles, drones, and more, solving real-world challenges.
- Hands-on experience: Develop innovative projects with advanced simulations and specialized equipment in our laboratories.
- Expert faculty: Learn from industry and academic leaders with extensive experience in research and development of autonomous systems.
- Career opportunities: Boost your career in high-demand sectors such as automotive, Aerospace, logistics, and software development.
- Become an expert in autonomous navigation and lead the next technological revolution.
Curriculum
Programme Structure
The programme is structured in 11 modules, listed below in order of study.
01
Fundamentals of mobile robotics, kinematics and dynamic modelling
- Introduction to Autonomous Navigation Systems: Levels of Autonomy (SAE J3016), Key Components and State of the Art
- Land vehicle kinematics: holonomic, non-holonomic, differential-wheel drive, Ackermann steering and traction hose
- Kinematics and dynamics of unmanned aircraft (multi-rotor UAS and fixed wing) and submarine vehicles (AUV)
- Geometric transformations in three-dimensional space: rotation matrices, Euler angles and quaternions
- Global and local coordinate systems: WGS84, ECEF, ENU, NED and vehicle frames fixed (Body Frame)
- Vehicular dynamics: pulling forces, pneumatic-terrain friction, weight transfer and curve stability
- Low-level actuation and control systems: stepper motors, servo motors, PWM control and frequency drives
- ROS/ROS2 (Robot Operating System) framework architecture: Nodes, themes, services, actions and middleware DDS
- Physical simulation of vehicles and dynamic environments using Gazebo, Isaac SIM and Carla Simulator
- Evaluation of kinematic performance, processing latency and energy consumption on embedded platforms
02
Inertial sensors, global positioning and odometry
- Operating Principles of Inertial Measurement Units (IMUs): MEMS accelerometres, gyroscopes and magnetometres
- Inertial sensor error characterisation: bias, drift, white noise, random walk and static/dynamic calibration
- Global Satellite Positioning Systems (GNSS): GPS, GLONASS, Galileo, Beidou and multi-frequency signals
- Sources of error in GNSS and correction techniques: DGPS, RTK (Real-Time Kinematic) and PPP (Precise Point Positioning)
- Wheel Odometry: Optical and Magnetic Encoders (Encoders), Displacement Calculation and Odometric Error Accumulation
- Inertial navigation systems (INS) and inertial machining for continuous estimation of position, speed and attitude (PVA)
- Tight and loose fusion (tightly-coupled / loosely-coupled) between GNSS and INS systems
- Magnetometry and electronic compass: Compensation of sweet iron and hard iron in metal structures
- Barometric Altimetres, Ultrasonic Distance Sensors, and Optical Flow Sensors for Low Altitude Navigation
- Positioning sensor redundancy for operations in zones with fading or spoofing GPS signal
03
Optical perception and computer vision systems
- Image sensors for autonomous navigation: CMOS technologies, CCD, event cameras, global shutter (Global Shutter) and Dynamic Range (HDR)
- Geometric and photometric camera calibration: pinhole camera model, radial/tangential distortion, and intrinsic/extrinsic arrays
- Stereoscopic vision: epipolar geometry, image rectification, disparity calculation and 3D depth maps
- Elementary image processing for navigation: edge detection, lane lines, optical flow, and feature point tracking (SIFT, ORB)
- Semantic segmentation and real-time instance by convolutional neural networks (CNN) applied to conduction (Yolo, Mask R-CNN)
- Detection and classification of obstacles, pedestrians, traffic signals and other vehicles in variable lighting conditions
- Estimate of motion by monocular, stereo and visual odometry (VO) based on characteristics or density of pixels
- Infrared and thermal (FLIR) cameras for night perception, fog and adverse weather conditions
- Continuous extrinsic alignment and calibration algorithms between multiple cameras with overlapping fields of view
- Accelerated processing of artificial vision through embarked GPUs and NPU (Neural Processing Units) architectures
04
LiDAR technology, radar and electroacoustic sensors
- Fundamentals of LiDAR (Light Detection and Ranging) Technology: Flight Time (TOF), Modulated Continuous Wave (FMCW) and Solid State LiDAR
- LiDAR 2D and 3D scan architecture: beam density, sampling frequency, range and response to reflective surfaces
- D Point Cloud Processing: Noise Filtering, Sampling (Voxel Grid), Earth Plane Segmentation, and Object Extraction
- Point Cloud Alignment Algorithms: ICP (Iterative Closest Point), NDT (Normal Distributions Transform) and its variants
- Principles of millimeter wave radar (MMWAVE) in automotive and robotics: distance detection, azimuth and direct Doppler speed
- Comparative advantages of radar versus lidar and vision in extreme weather (rain, snow, dust, fog)
- Sonar and ultrasonic sensors: acoustic propagation in air and water, distance profiling and ultrasonic matrix for low-speed manoeuvres
- Spatial and temporal cross-calibration methods between LiDAR, radar and cameras
- Interaction of optical and electromagnetic radiation with materials: absorption, dispersion and false positives by glare
- Sensor combination selection criteria (Sensor Suite) based on cost, weight, consumption (swap) and operational requirements
05
Advanced sensory fusion and state estimation
- Probabilistic estimation theory: Random variables, Gaussian distributions, and uncertainty representation in robotics
- Classic Kalman Filter (KF): Mathematical deduction, covariance matrices, and state prediction/update
- Extended Kalman Filter (EKF) for non-linear systems: Jacobian matrices linearization and guidance applications
- Kalman Unscented Filter (UKF): Unscented Transformation, Sigma Points and Estimation in Highly Nonlinear Systems
- Particle filters (Sequential Monte Carlo): Estimation of non-Gaussian states and location in ambiguous environments
- Factor graphs based data fusion (Graphs Factor) and Non-Linear Optimisation Not Adjusted (Bundle Adjustment)
- LiDAR-Camera-IMU-GNSS Multisensory Fusion: Early Fusion (Early Fusion) and Late Fusion (Late Fusion)
- Detection and rejection of anomalous measurements (Outliers): Chi-square test, Mahalanobis distance and Ransac algorithms
- Data mapping and multi-object tracking (MOT): MHT algorithms, JPDA and PHD filters applied to the dynamic environment
- Sensory fusion performance assessment: Medium quadratic error (RMSE), filter consistency, and output latency
06
Mapping, simultaneous location and navigation without GPS (SLAM)
- The problem of location and mapping in robotics: Bayesian SLAM formulation and drift correction cycle
- D Occupation Grid Mapping and Octree 3D Probabilistic Maps (Octomap) for free space representation
- Inertial-Visual Slam (VI-SLAM): Monocular and Stereo Architectures (ORB-SLAM3, VINS-MONO, OKVIS)
- SLAM based on LiDAR 3D: Loam, LEGO-LOAM, LIO-SAM and Inertial LiDAR (LIO) odometry algorithms
- Loop Closure Detection Detection: Bag of Visual Words (BOVW), Global Descriptors, and Accumulated Drift Correction
- Pose Graph Optimisation Optimisation: Use of G2O and GTSAM libraries for trajectory error minimisation
- HD (High-Definition Maps) Mapping for Autonomous Driving: Semantic Layers, Georeferencing and Dynamic Cloud Update
- HD map-based location: LiDAR/Camera real-time alignment against pre-built feature maps
- Autonomous navigation in closed environments (GPS-Denied): tunnels, underground mines, industrial buildings and urban canyons
- Benchmarking and evaluation of SLAM systems using public data sets (Kitti, Euroc, Tum VI, Nuscenes)
07
Path planning, decision making and dynamic control
- Motion Planning Hierarchy: Mission Planning, Global Route Planning, and Local Manoeuvre Planning
- Route search algorithms in graphs: Dijkstra, A*, Searching A* (D*) and state space optimisation algorithms
- Random Sampling Algorithms for High Dimension Spaces: RRT (Rapidly-Exploring Random Trees), RRT* and PRM (Probabilistic Roadmaps)
- Generation of smooth and executable paths kinematically: Bezier curves, B-splines, Dubins curves and Reeds-Shepp
- Dynamic Obstacle Avoidance: Dynamic Window Algorithm (DWA), Artificial Potential Fields, and Speed Spaces
- Decision-making in dynamic and traffic environments: Finite-state machines (FSM), behaviour trees and Markov decision processes (MDP)
- Path Tracking Control: Pure Pursuit Controller, Stanley Controller and PID Controllers Applied to Direction and Acceleration
- Model-based predictive control (MPC): Mathematical formulation, kinematic/dynamic restrictions and real-time optimisation
- Planning of cooperative trajectories and swarming (Swarm Robotics) for multiple autonomous agents
- Strategies for manoeuvreing of emergency stop and transition to states of minimum risk (Minimum Risk Manoeuvre)
08
System architectures, embedded computing and communication networks
- Autonomous Vehicle Hardware Architectures: Electronic Control Units (ECU), FPGAs, ASICS, and Heterogeneous Platforms (NVIDIA Jetson, Drive, Qualcomm Snapdragon Ride)
- Real-time operating systems (RTOs) and critical execution threads on stand-alone navigation platforms
- Internal vehicle communication networks: CAN bus, CAN-FD, Flexray, LIN and Automotive Ethernet (100/1000BASE-T1)
- Low Latency Communication Middleware: DDS (Data Distribution Service), SOME/IP, and Interprocess Shared Memory
- Edge computing versus cloud computing in autonomy telemetry
- Hardware Temporary Synchronization Protocols: PTP (Precision Time Protocol - IEEE 1588) and PPS signals for sensor time sealing
- Management of thermal consumption, power and design of marinated/rogged mechanical enclosures (IP67/69K standards, MIL-STD-810)
- Service-oriented software architecture (SOA) and Autosar (Classic and Adaptive) standards in the automobile industry
- Vehicle-to-all communications (V2X): V2V, V2I, C-V2X and DSRC for cooperation in intersections and platoons (Platooning)
- Monitoring of system health status (System Health Monitoring) and on-board diagnostics (OBD-II / UDS)
09
Functional security, cybersecurity, validation and regulatory framework
- Functional safety in autonomous automotive systems: ISO 26262 standard and safety integrity levels (ASIL A-D)
- Security of the intended functionality (Safety of the Intended Functionality - SOTIF / ISO 21448) in the presence of sensory limitations
- Failure mode analysis, its effects and diagnostics (FMEA/FMEDA) and Fault Tree Analysis (FTA) in Navigation Robotics
- Autonomous vehicle cybersecurity: ISO/SAE 21434 and threat assessment and risk analysis (TARA)
- Autonomous sensor vulnerabilities: Laser noise injection (Lidar Spoofing), Blind cameras and GNSS impersonation
- Secure Cyber Architectures: Secure Boot, Hardware Security Modules (HSM) and CAN/Ethernet bus encryption
- Test and validation methodologies: Software-in-the-loop (SIL), hardware-in-the-loop (HIL) and vehicle-in-the-loop (VIL) testing
- Generation of critical test scenarios through probabilistic simulation and Edge-Case Mining methods
- International Autonomous Transport Regulatory Framework: UNECE/UN Regulations (ALKS), FAA regulations for drones and IMO Mass maritime legislation
- Ethics in the systems of autonomous decision, moral dilemmas of navigation and legal responsibility in the event of a collision
10
Capstone Project: Design, development and validation of a self-contained navigation system
- Selection and definition of the practical application case: land vehicle (AGV/Robotaxi), aerial drone (UAV) or autonomous ship (USV)
- Analysis of functional, operational requirements (ODD - Operational Design Domain) and security architecture specification
- Sensor Suite Design (Sensor Suite) and Geometric Coverage Calculation, Redundancy and Consumption/Weight Budgets
- Integration and spatial and temporal calibration of the sensor network in the test vehicle or simulation platform
- Implementation of the sensory fusion and perception module for the detection and classification of obstacles in real time
- Development and integration of the localization and mapping system (SLAM / GNSS-INS fusion) in the selected environment
- Programming the local/global path planning stack and dynamic collision avoidance algorithm
- Setting the dynamic guidance control algorithms (MPC/PID) in a photorealistic simulation environment (Gazebo/Carla)
- Experimental validation of the system under HIL/SIL tests and technical evaluation of navigation accuracy and failure rate
- Preparation of the Technical Report of Engineering, Security and Public Defence Manual of the Capstone Project before Expert Court
11
Master’s Final Project in Sensors and Autonomous Navigation Systems
- Choice of topic and definition of the research problem
- Formulation of objectives, hypotheses and scope of work
- Review of the state of the art and specialised bibliography
- Analysis of the applicable regulatory and technical framework
- Design of the methodology and the work plan
- Data collection, processing and validation
- Technical development and analysis of results
- Discussion, conclusions and future lines of work
- Drafting of the memory in accordance with academic standards
- Presentation and public defence before the Court
Workload
Study Time and Workload
1600 hours of study, online and at your own pace. Move the slider to the hours a week you can really put in.
Hours a week8
Weeks to finish—
You finish around—
Who it is for
Electronic, computer, and telecommunications engineers seeking specialization in environmental perception, data fusion, and autonomous control.Automotive, robotics, and aerospace professionals interested in adapting autonomous navigation technologies to new domains.Researchers and academics wishing to delve deeper into the latest advances in sensors, algorithms, and autonomous system architect
Faculty
Academic direction and faculty
The teaching team at Navalis: working professionals who deliver and oversee the institute's programmes.
Eng. Diego Cifuentes
Cap. Javier Abaroa (MCA)
Dr. Pau Ferrer
Dr. Nuria Llobregat
Eng. Javier Bañuls
Eng. Sofía Marquina
Eng. Tomás Riera
Verified reviews
Student Reviews
Reviews collected and verified by Opynio, not written by us.
Outcomes
Learning Outcomes
- 01Design and implement advanced perception systems
- 02Develop robust and efficient sensor fusion algorithms
- 03Managing and optimizing the performance of autonomous navigation systems
- 04Integrate and validate navigation systems in real-world environments
- 05Lead R&D projects in the field of mobile robotics and navigation.
- 06Evaluate and select the most suitable sensor technology for each application
Careers
Career Opportunities
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02Sensor Development Engineer: Design, prototyping, and validation of sensors for autonomous navigation.
03Navigation Systems Engineer: Integration of sensors, sensor fusion algorithms, and control systems.
04Mobile Robotics Specialist: Development of navigation algorithms, route planning, and control of autonomous robots.
05Test and Validation Engineer: Test design, data analysis, and performance evaluation of autonomous systems.
06Computer Perception Data Scientist: Development of computer vision algorithms, machine learning, and signal processing.
07Autonomous Driving R&D Engineer: Development of perception, planning, and control systems for autonomous vehicles.
08Autonomous Systems Consultant: Technical consulting in the implementation of autonomous navigation solutions in various sectors.
09Technology Entrepreneur: Creating innovative companies in the field of robotics and autonomous navigation.
Questions
Entry Requirements
Sensor systems and autonomous navigation.
Yes. The itinerary includes ECDIS/Radar-ARPA/BRM with harbor, ocean, fog, storm, and SAR scenarios.
Online with live sessions; hybrid option for simulator/practical placements through agreements.
Robotics engineering, specifically autonomous navigation.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna Institute + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
Online admissions
Admissions Process
No waiting for a phone call. If your documents are complete, you can pay in the same session.
Entry requirements
```htmlAcademic/Professional Profile:Bachelor's Degree in Engineering (Electronics, Computer Science, Mechatronics, Telecommunications, Aerospace, etc.), Physics, Mathematics, or a related scientific-technical field. Demonstrable professional experience in sensor development, robotics, or autonomous navigation.Technical Skills:Solid knowledge of mathematics (linear algebra, calculus, statistics), physics, and programming (C++, Python, ROS). Familiarity with sensors (IMU, GPS, LiDAR, cameras), signal processing algorithms, and computer vision.Language Proficiency:English level (B2
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Frequently Asked Questions
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