  This paper presents the OmniRace approach to controlling a racing drone with
6-degree of freedom (DoF) hand pose estimation and gesture recognition. To our
knowledge, it is the first-ever technology that allows for low-level control of
high-speed drones using gestures. OmniRace employs a gesture interface based on
computer vision and a deep neural network to estimate a 6-DoF hand pose. The
advanced machine learning algorithm robustly interprets human gestures,
allowing users to control drone motion intuitively. Real-time control of a
racing drone demonstrates the effectiveness of the system, validating its
potential to revolutionize drone racing and other applications. Experimental
results conducted in the Gazebo simulation environment revealed that OmniRace
allows the users to complite the UAV race track significantly (by 25.1%) faster
and to decrease the length of the test drone path (from 102.9 to 83.7 m). Users
preferred the gesture interface for attractiveness (1.57 UEQ score), hedonic
quality (1.56 UEQ score), and lower perceived temporal demand (32.0 score in
NASA-TLX), while noting the high efficiency (0.75 UEQ score) and low physical
demand (19.0 score in NASA-TLX) of the baseline remote controller. The deep
neural network attains an average accuracy of 99.75% when applied to both
normalized datasets and raw datasets. OmniRace can potentially change the way
humans interact with and navigate racing drones in dynamic and complex
environments. The source code is available at
https://github.com/SerValera/OmniRace.git.
