UAV Posture Recognition via Depth Image Point Cloud Segmentation
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Solution Overview
Problem
Current methods for controlling unmanned aerial vehicles (UAVs) are complex and non-intuitive, relying on external devices or imprecise hand gesture recognition, which leads to poor user experience and tracking issues.
Innovation Solution
A method involving the acquisition of depth images to recognize the posture of an operator, separating arm point clouds, and determining characteristic points to control the UAV based on the location relationship, allowing for intuitive control through natural gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hand gesture recognition is used to control UAV, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transitions from 2D image-based gesture recognition to 3D depth image-based recognition. By acquiring depth information and constructing 3D point clouds of the operator's body and limbs, the system achieves more accurate spatial understanding of gestures, resolving the precision issue while maintaining ease of operation through natural body movements.
Solution Approach 2:
The patent replaces traditional mechanical control interfaces (remote controllers) and simple 2D computer vision systems with a depth sensing system (such as time-of-flight cameras or structured light sensors). This substitution enables precise 3D gesture recognition, improving measurement precision while keeping the control method intuitive and easy to operate.
2Reliability
If external devices are used to control UAV, then reliability is improved, but device complexity is worsened
Solution Approach 1:
The patent extracts the control interface from external devices and integrates it directly into the UAV system through onboard depth sensors. By taking out the gesture recognition capability and embedding it in the UAV, the system eliminates the need for separate remote controllers or external computing devices, reducing overall device complexity while maintaining reliable control through direct sensor-data processing integration.
Solution Approach 2:
The patent makes the UAV's sensor system multi-functional by using the depth sensing capability for both navigation/environmental perception and gesture recognition/control. This universality eliminates the need for dedicated external control devices, simplifying the overall system while ensuring reliable control functionality is built-in rather than dependent on external equipment.
3Measurement precision
If depth image processing is implemented, then measurement precision is improved, but use of energy is worsened
Solution Approach 1:
The patent segments the operator's point cloud into distinct body parts (torso, arms, legs) and focuses processing only on relevant segments for gesture recognition. By dividing the processing task into targeted segments rather than analyzing the entire scene or all points, the system maintains high measurement precision for gesture detection while significantly reducing the computational energy required compared to full-scene depth image processing.
Data Source
AI summary
A method for recognizing a posture includes acquiring a depth image of a scene, and obtaining point clouds of an operator based on the depth image of the scene. The method also includes separating point clouds of an arm from the point clouds of the operator and obtaining a characteristic point from the point clouds of the arm. The method further includes determining a location relationship between the characteristic point and the operator and determining a posture of the operator based on the location relationship.