Deep Learning on accumulated LiDAR PointCloud Internship Toulouse
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Monthly Salary:
Posted on:
12 hours ago
Vacancies:
1 Vacancy
Job Summary
Internship details
Duration: 6 months
Start date: ASAP
Location: Toulouse
Team: Perception
Internship subject: Deep Learning on accumulated LiDAR PointClouds
Compensation: 1000 gross tickets restaurant CSE
Internship Context
EasyMile is advancing Level 4 autonomous technology allowing our vehicles to operate fully driverless in defined environments by independently managing a lot of driving situations. Safe operation requires a high volume of environmental information which is gathered and processed in real-time by a full range of on-board sensors like LiDAR RADAR and cameras. These sensors create a complete 360-degree environmental model capturing infrastructure moving adverse that the driverless system uses to make safe progression decisions.
In the Perception Team we use deep learning techniques to analyze this surrounding environment. Currently our scene understanding stack runs online using few sensor scans; however accumulating LiDAR point clouds along the vehicles trajectory especially with newer sensors allows us to represent a dense 3D scene that can significantly elevate our final performance.
This internship focuses on applying Deep Learning techniques to these large dense PointClouds of our vehicle environment. You will explore recent work like the Superpoint Transformer 1 and the lightweight EZ-SP 2 to help us redefine our offline perception stack specifically aiming to improve our auto-annotation process for static obstacles enhance the automatic creation of HD Maps and potentially develop a new format for semantic prior maps for our autonomous vehicles.
1 Robert D. Raguet H. & Landrieu L. (2023). Efficient 3D Semantic Segmentation with Superpoint Transformer.
2 Geist L. Landrieu L Robert D (2025). EZ-SP: Fast and Lightweight Superpoint-Based 3D Segmentation
Missions / Responsabilities
Under the supervision of his tutor the intern will be involved in:
Format the easymile data for large pointcloud training
Study the literature to find relevant architecture and network
Train and evaluate such network on possible different use cases: static object detection and/or segmentation HDMap generation
Required Experience:
Intern
Key Skills
- Aerospace Engineering
- Data Entry
- Business Management
- Flexcube
- Contract
About Company
Driverless vehicle solutions and full-service autonomous technology for smart mobility in residential areas, campuses and business parks, as well as material handling in factories and industrial sites.
