Master Thesis: Multimodal Sensor Fusion and Robust Odometry for XR Perception
Technology, Data & Digital · Software & Web Development · Software Engineering · Robotics & Automation
In short
Develop a robust perception and motion-estimation system for XR platforms in challenging environments by fusing multimodal sensors like cameras, LiDAR, and IMUs. This Master's thesis project involves reviewing and integrating existing algorithms, implementing sensor fusion pipelines, and evaluating system performance under various conditions.
Responsibilities
- Review approaches for visual-inertial odometry, LiDAR odometry, SLAM, and multimodal sensor fusion, and define a suitable architecture for the NEXSoS XR platform.
- Integrate available sensing hardware and develop calibration, synchronisation, preprocessing, and coordinate-transformation components.
- Implement or adapt a sensor-fusion and odometry pipeline using frameworks such as ORB-SLAM3, RTAB-Map, or other relevant methods.
- Evaluate the system under different environmental conditions.
- Compare individual sensors with multimodal configurations in terms of accuracy, robustness, latency, and computational requirements.
- Analyse and visualise trajectories, point clouds, depth maps, and 3D reconstructions.
- Document system limitations and provide recommendations for future development.
Requirements
- Enrolled in or recently admitted to a Master's programme in Robotics, Computer Science, Electrical Engineering, or a related field.
- Basic knowledge of computer vision, robotics, estimation, 3D perception, or signal processing.
- Programming experience in Python and/or C++.
- Understanding of coordinate transformations, rigid-body motion, and sensor data processing at a basic level.
- Interest in working with real sensors, experimental hardware, and research software.
- Ability to analyse experimental results and communicate technical findings clearly.
Desired Qualifications
- Experience with visual, inertial, or LiDAR odometry, or SLAM.
- Familiarity with ROS/ROS 2, ORB-SLAM3, RTAB-Map, OpenCV, Open3D, PCL, or similar tools.
- Knowledge of Kalman filtering, factor graphs, nonlinear optimisation, or bundle adjustment.
- Experience with camera–IMU or LiDAR–camera calibration.
- Previous experience with thermal, infrared, depth, or LiDAR sensors.
Benefits
- Master Thesis project contribution to the NEXSoS XR project.
#XR#Perception#Odometry#Sensor Fusion#Robotics#Computer Vision#Master Thesis
Company
Ericsson
Job Posted
14 hours ago
Employment Type
Internship
WorkMode
On Site
Experience Level
Entry
Locations
Stockholm, Sweden
Qualification
Master
Applicants
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