Motion Estimation and Hand Gesture Recognition-Based Human–UAV Interaction Approach in Real Time

Abstract

As an alternative to traditional remote controller, research on vision-based hand gesture recognition is being actively conducted in the field of interaction between human and unmanned aerial vehicle (UAV). However, vision-based gesture system has a challenging problem in recognizing the motion of dynamic gesture because it is difficult to estimate the pose of multi-dimensional hand gestures in 2D images. This leads to complex algorithms, including tracking in addition to detection, to recognize dynamic gestures, but they are not suitable for human–UAV interaction (HUI) systems that require safe design with high real-time performance. Therefore, in this paper, we propose a hybrid hand gesture system that combines an inertial measurement unit (IMU)-based motion capture system and a vision-based gesture system to increase real-time performance. First, IMU-based commands and vision-based commands are divided according to whether drone operation commands are continuously input. Second, IMU-based control commands are intuitively mapped to allow the UAV to move in the same direction by utilizing estimated orientation sensed by a thumb-mounted micro-IMU, and vision-based control commands are mapped with hand’s appearance through real-time object detection. The proposed system is verified in a simulation environment through efficiency evaluation with dynamic gestures of the existing vision-based system in addition to usability comparison with traditional joystick controller conducted for applicants with no experience in manipulation. As a result, it proves that it is a safer and more intuitive HUI design with a 0.089 ms processing speed and average lap time that takes about 19 s less than the joystick controller. In other words, it shows that it is viable as an alternative to existing HUI.

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“…In [ 10 ], the authors employed sensor fusion between a mechanomyography band and a hand-mounted initial measurement unit (IMU) to achieve robust control of an aerial drone using only the mechanical motion of the hand. In [ 11 ], the authors utilised a single RGB camera with marker gloves to recognise static gestures in combination with a hand-mounted IMU to recognise the dynamic motion of these gestures. In [ 12 ], the authors used a single RGB camera input paired with MPH and a long short-term memory neural network to achieve intuitive drone control that required no calibration or specialised equipment.…”