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Learned Perception for Drone Racing
Learned Perception for Drone Racing
In autonomous racing, the quadrotor drone must pass through a sequence of gates as fast as possible. A major challenge is detecting and identifying relevant objects - such as racing rates and colliders - using an onboard camera even under challenging lighting conditions and motion blur.
In autonomous racing, the quadrotor drone must pass through a sequence of gates as fast as possible. A major challenge is detecting and identifying relevant objects - such as racing rates and colliders - using an onboard camera even under challenging lighting conditions and motion blur.
The goal of this project is to use machine learning to develop a robust object detector for drone racing and to evaluate its performance for vision-based autonomous drone racing. Requirements: Strong background in machine learning, computer vision, and robotics. Pytorch, Tensorflow, Python, and ROS skills. Experience with quadrotor hardware and drone flight is a plus.
The goal of this project is to use machine learning to develop a robust object detector for drone racing and to evaluate its performance for vision-based autonomous drone racing. Requirements: Strong background in machine learning, computer vision, and robotics. Pytorch, Tensorflow, Python, and ROS skills. Experience with quadrotor hardware and drone flight is a plus.
Please send your CV and transcripts (bachelor and master) to Christian Pfeiffer (cpfeiffe AT ifi DOT uzh DOT ch) and Yunlong Song (song AT ifi DOT uzh DOT ch).
Please send your CV and transcripts (bachelor and master) to Christian Pfeiffer (cpfeiffe AT ifi DOT uzh DOT ch) and Yunlong Song (song AT ifi DOT uzh DOT ch).