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IMU-centric Odometry for Drone Racing and Beyond
IMU-centric Odometry for Drone Racing and Beyond
Keywords: Computer Vision, Visual-Inertial Odometry, Drone Racing
Our recent work has shown that it is possible to estimate the state of a racer drone only using a low-grade IMU.
This project will build upon our previous work and try to extend its applicability to scenarios beyond racing.
To achieve this goal, we will investigate an "unconventional" way of using camera images inside the odometry pipeline.
The developed VIO pipeline will be compared to existing state-of-the-art model-based algorithms, with a focus on application in agile flights in the wild, and deployed on embedded platforms (Nvidia Jetson TX2 or Xavier).
Our recent work has shown that it is possible to estimate the state of a racer drone only using a low-grade IMU. This project will build upon our previous work and try to extend its applicability to scenarios beyond racing. To achieve this goal, we will investigate an "unconventional" way of using camera images inside the odometry pipeline. The developed VIO pipeline will be compared to existing state-of-the-art model-based algorithms, with a focus on application in agile flights in the wild, and deployed on embedded platforms (Nvidia Jetson TX2 or Xavier).
Development of an IMU-centric odometry algorithm. Benchmark against state-of-the-art VIO method. A successful thesis will lead to the deployment of the proposed odometry algorithm on the real drone platform. We look for students with strong programming (C++ preferred), computer vision (ideally have taken Prof. Scaramuzza's class), and robotic background. Hardware experience (running code on robotic platforms) is preferred.
Development of an IMU-centric odometry algorithm. Benchmark against state-of-the-art VIO method. A successful thesis will lead to the deployment of the proposed odometry algorithm on the real drone platform. We look for students with strong programming (C++ preferred), computer vision (ideally have taken Prof. Scaramuzza's class), and robotic background. Hardware experience (running code on robotic platforms) is preferred.