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Data-Driven Visual Inertial Odometry for Quadrotor Flight
Investigate the usability of data-driven methods to improve the performance of a VIO pipeline on a resource-constrained platform.
Classical VIO pipelines use geometric information to infer the ego-motion of the camera and couple this information with measurements from the IMU. While these pipelines have shown very good performance in controlled, structured environments, their performance decreases when applied in low-texture or dynamic environments or when applied to high-speed motion.
Recent works propose the usage of data-driven approaches for camera ego-motion estimation. While such approaches could potentially learn a VIO pipeline end-to-end, their generalizability is not good enough for real-world deployment.
This work investigates the usage of a hybrid VIO pipeline featuring a learned visual frontend.
Requirements:
- Background in computer vision and machine learning
- Deep learning experience preferable but not strictly required
- Programming experience in C++ and Python
Classical VIO pipelines use geometric information to infer the ego-motion of the camera and couple this information with measurements from the IMU. While these pipelines have shown very good performance in controlled, structured environments, their performance decreases when applied in low-texture or dynamic environments or when applied to high-speed motion.
Recent works propose the usage of data-driven approaches for camera ego-motion estimation. While such approaches could potentially learn a VIO pipeline end-to-end, their generalizability is not good enough for real-world deployment.
This work investigates the usage of a hybrid VIO pipeline featuring a learned visual frontend.
Requirements: - Background in computer vision and machine learning - Deep learning experience preferable but not strictly required - Programming experience in C++ and Python
Based on results from a previous student project, the goal is to deploy a hybrid VIO pipeline on a quadrotor equipped with a GPU (Jetson TX2).
Based on results from a previous student project, the goal is to deploy a hybrid VIO pipeline on a quadrotor equipped with a GPU (Jetson TX2).
Elia Kaufmann (ekaufmann@ifi.uzh.ch); Philipp Foehn (foehn@ifi.uzh.ch)
Elia Kaufmann (ekaufmann@ifi.uzh.ch); Philipp Foehn (foehn@ifi.uzh.ch)