We are looking for a motivated student to join our research on adaptive visual processing in SLAM systems. Building on our recent AFDI-SLAM framework, this project aims to develop more fine-grained frame rate modulation strategies based on motion dynamics, with a focus on decoupling translational and rotational cues, real-time performance, and intelligent scheduling.

Ideal candidates should have experience in computer vision, robotics, or deep learning. Familiarity with SLAM, PyTorch, or CUDA is a plus. This is a great opportunity to contribute to a practical, high-impact system and publish in top multimedia or robotics venues.

Voraussetzungen

In Visual-Inertial SLAM (Simultaneous Localization and Mapping), inertial measurement units (IMUs) are crucial for estimating motion. However, IMU data often contains accumulative noise, which degrades SLAM performance. Self-supervised machine learning techniques can automatically denoise IMU data without requiring labeled datasets. By leveraging self-supervised training, the project aim to explore neural networks distinguish useful IMU signal patterns from noise, improving the accuracy of motion estimation and robustness of Visual-Inertial SLAM systems.

Voraussetzungen