Master thesis proposal
Semantic Mapping, Navigation, and Obstacle Avoidance for the Autonomous Platform
PURPOSE OF THE STUDY
To increase the knowledge and expertise within the evolving automotive industry, Infotiv Technology Development AB developed the open and educational Autonomous Platform. It is currently in its fourth iteration (AP4) and the goal of the platform is to have an autonomous go-kart that can be used to progress research and development in the automotive industry.
The purpose of the study is to design and implement a full perception-to-navigation pipeline on the AP, combining simultaneous localisation and mapping (SLAM) with multi-sensor fusion, semantic object detection, and intelligent path planning. Rather than relying on pre-built maps or reactive obstacle avoidance alone, the platform should be capable of incrementally building a map, populating it with detected objects, and exploiting visual landmarks to plan paths that are both safe and contextually aware. The goal is to evaluate and improve the effectiveness of the Autonomous Platform for one or several of the items below:
- Building and maintaining an accurate map of the environment using SLAM with fused data from LiDAR, camera, IMU or Laser
- Detecting, classifying, and localising objects in the environment and storing them as semantic annotations within the map
- Using visual cues, such as lane markings, signage, and recognisable landmarks , to improve localisation accuracy and path selection
- Planning routes that account for both static map knowledge and dynamically detected obstacles
- Validating the full system on the physical AP hardware and inside the existing simulation environment
POTENTIAL RESEARCH QUESTIONS
- How does fusing LiDAR with camera data and other sensors affect SLAM accuracy and map completeness compared to single-sensor approaches on the AP?
- How can detected objects (dynamic obstacles) be represented and stored in the navigation map so that the platform avoids them reliably across multiple traversals of the same area?
- What visual cues available in the AP's operating environment provide the most reliable information for safe path selection?
- How does the navigation system perform when obstacles appear dynamically, and how quickly can the planner re-route to maintain safety?
- What are the computational constraints of running a full SLAM and semantic mapping pipeline on the AP's onboard hardware, and how can the system be optimised to meet real-time requirements?
Learn more about the autonomous platform project in this GitHub repository:
GitHub - infotiv-research/autonomous_platform
WHO ARE WE LOOKING FOR?
We are looking for 2 master’s students with a background in mechatronics, electrical engineering, or equivalent program (e.g., MPSYS, MPCAS, MPDSC), who wish to conduct their thesis during the spring of 2027. Applicants shall have experience in electrical engineering, software development, and an interest in learning more about test automation. Applicants should also have experience in programming languages such as C++ or Python. It is meritorious to have previous experience from or knowledge of ROS 2, Nav2, SLAM toolkits, object detection frameworks, sensor fusion, Git, or Docker. Automation, ROS2 and Machine learning.
ABOUT US
TechDev is a department at Infotiv who focuses on SW & HW development and test solutions. We currently consist of 70 technical consultants with diverse backgrounds and experience from many technological fields. Our employees use their expertise to provide tailored solutions to all kinds of challenges, ranging from SW development, machine learning and simulations to project & test management and way of working. One of our key strengths is the friendly atmosphere in our technical community, which provides access to TechDev's collective knowledge through internal collaboration tools and competence leader programs, continuously providing updates in the latest tech.
HOW TO APPLY
Apply for this thesis no later than 2026-12-31. Assure to attach your resumé and a short summary of why you want to partake in this thesis.
For further information, contact: Maria Alemyr [maria.alemyr@infotiv.se] +46(0)-76 890 78 72
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