2027 Internship State Estimation, Learned Mapping & Semantic SLAM

What you'll do

• Develop learned SLAM and mapping methods, including place recognition, odometry, depth completion, and neural scene representations.
• Train models and build evaluation pipelines for mapping and localization using fleet lidar and camera data.
• Deliver a documented prototype, experimental results, and recommendations, collaborating with perception and planning teams.

What they ask for

• Pursuing a BS, MS, or PhD in computer science, robotics, electrical engineering, or a related field, or equivalent research or industry experience.
• Strong Python skills and hands-on model training experience with PyTorch or a similar framework.
• Solid understanding of 3D geometry, coordinate frames, and transforms.
• Familiarity with SLAM and mapping fundamentals, point clouds, or depth data.

Nice to have

• Research or project experience in learned SLAM, semantic mapping, or 3D scene understanding.
• Experience with neural scene representations, vision foundation models, lidar processing, multi-sensor fusion, field robotics data, Rust or C++, or ROS or similar middleware.

Tools

Python, PyTorch, NeRFs, 3D Gaussian splatting, DINOv2, SAM, Rust, C++

AI focus

Applies learning-based methods and vision foundation models to SLAM, mapping, localization, and semantic understanding of construction terrain.