Thesis project: Pallet perception through deep learning and sensor fusion
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Toyota Material Handling Europe AB Thesis project: Pallet perception through deep learning and sensor fusion
Develop methods using deep learning models to improve the current pallet perception system, with a focus on fusing model outputs with data from other sensors. The main focus will be on general pallet identification and localization.
In your Master Thesis at Toyota, you will work on:
A modern sensor system featuring:
2D RGB camera
Time-of-Flight (ToF) 3D camera
Correlation between pixels from the RGB camera and points in the 3D point cloud from the ToF camera
NVIDIA chip in an embedded environment (GPU) for data processing
Tasks include:
Training and evaluation of deep learning (DL) models using different methodologies
Algorithm development for data fusion
Evaluation using real-world data and comparison with training results from a synthetic environment
At Toyota, we work with innovative technologies to develop our autonomous trucks. The thesis project will investigate training methodologies, model architecture, and the selection of suitable model outputs that can be effectively combined with point cloud data to accurately localize a pallet in relation to the camera position. The training data will be generated in synthetic environments, which will be provided as part of the project. Therefore, the gap between synthetic and real-world data will also need to be considered when evaluating the results. The thesis project will be carried out in close collaboration with one of Toyota's teams specializing in machine learning development.
SCOPE
Master thesis 30 hp, 1-2 students
Requirements
For this master thesis, we are looking for students with following education or equivalent:
3D Computer vision
Deep learning specialization / Machine Learning specialization
Who is Toyota Material Handling?
Toyota Material Handling is a global leader in material handling, and we are making significant investments to meet the needs of the future. At our site in Mjölby, 3,000 employees work across the entire material handling value chain, from development concepts to finished vehicles. Our product range spans from manual hand trucks to autonomous vehicles and innovative energy solutions.
At Toyota Material Handling, we strive to create a friendly, safe and forward-thinking workplace. Our culture is built on Toyota's core values, where respect and consideration guide us in our daily work. Our ambition is to strengthen our competitiveness by increasing diversity across the organization and embracing our differences.
Start
January 2027
Your application:
Your application is individual. If you plan to do the master thesis together with another student, please specify name of that person. Latest date for application 2026-11-01.
Your application can be written in English or Swedish.
If you have any questions, please contact:
Axel Carlsson, Machine Learning Engineer, 073 052 99 63
Albin Sidås , Machine Learning Architect, 072 227 79 31
Josefin Nilsson, HR, josefin.nilsson@toyota-industries.eu
Instagram: ToyotaMHsweden
Linkedin: Toyota Material Handling Manufacturering Sweden AB
#MS
In your Master Thesis at Toyota, you will work on:
A modern sensor system featuring:
2D RGB camera
Time-of-Flight (ToF) 3D camera
Correlation between pixels from the RGB camera and points in the 3D point cloud from the ToF camera
NVIDIA chip in an embedded environment (GPU) for data processing
Tasks include:
Training and evaluation of deep learning (DL) models using different methodologies
Algorithm development for data fusion
Evaluation using real-world data and comparison with training results from a synthetic environment
At Toyota, we work with innovative technologies to develop our autonomous trucks. The thesis project will investigate training methodologies, model architecture, and the selection of suitable model outputs that can be effectively combined with point cloud data to accurately localize a pallet in relation to the camera position. The training data will be generated in synthetic environments, which will be provided as part of the project. Therefore, the gap between synthetic and real-world data will also need to be considered when evaluating the results. The thesis project will be carried out in close collaboration with one of Toyota's teams specializing in machine learning development.
SCOPE
Master thesis 30 hp, 1-2 students
Requirements
For this master thesis, we are looking for students with following education or equivalent:
3D Computer vision
Deep learning specialization / Machine Learning specialization
Who is Toyota Material Handling?
Toyota Material Handling is a global leader in material handling, and we are making significant investments to meet the needs of the future. At our site in Mjölby, 3,000 employees work across the entire material handling value chain, from development concepts to finished vehicles. Our product range spans from manual hand trucks to autonomous vehicles and innovative energy solutions.
At Toyota Material Handling, we strive to create a friendly, safe and forward-thinking workplace. Our culture is built on Toyota's core values, where respect and consideration guide us in our daily work. Our ambition is to strengthen our competitiveness by increasing diversity across the organization and embracing our differences.
Start
January 2027
Your application:
Your application is individual. If you plan to do the master thesis together with another student, please specify name of that person. Latest date for application 2026-11-01.
Your application can be written in English or Swedish.
If you have any questions, please contact:
Axel Carlsson, Machine Learning Engineer, 073 052 99 63
Albin Sidås , Machine Learning Architect, 072 227 79 31
Josefin Nilsson, HR, josefin.nilsson@toyota-industries.eu
Instagram: ToyotaMHsweden
Linkedin: Toyota Material Handling Manufacturering Sweden AB
#MS