Multi-Camera UAV Auto-Return with Orientation-Independent Path Mapping
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Solution Overview
Problem
Aerial vehicles equipped with a single camera face challenges in auto-returning due to differences in images recorded during flight and return paths, leading to potential navigation errors without reliable external communication signals.
Innovation Solution
Equipping unmanned aerial vehicles (UAVs) with multiple cameras providing different fields of view to collect and compare image data, using feature points for navigation, and selectively storing image features based on parameters like movement and overlap to reduce processing demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera is used for aerial photography, then the device complexity is reduced, but the reliability of auto-return navigation deteriorates due to image segmentation and inability to recognize path regardless of orientation
Solution Approach 1:
The patent divides the imaging function into multiple cameras with different fields of view (forward-facing, backward-facing, sideways-facing cameras). This segmentation allows the system to capture images from multiple directions simultaneously, enabling reliable path recognition regardless of the vehicle's orientation during auto-return
Solution Approach 2:
The multi-camera system provides universal imaging capability in all directions. Each camera type (forward, backward, sideways) contributes to a comprehensive view that enables the auto-return function to work reliably under any orientation condition, making the navigation system universally applicable regardless of vehicle attitude
2Reliability
If multiple cameras are used to provide comprehensive environmental coverage, then the reliability of auto-return navigation is improved, but the processing and memory storage burden increases
Solution Approach 1:
The patent extracts only the essential navigation information (feature points) from the images captured by multiple cameras, rather than processing all image data. This extraction approach maintains navigation reliability while significantly reducing the processing and storage burden by focusing only on discriminative features needed for path recognition
Solution Approach 2:
The system performs partial processing by selecting and storing only certain feature points from images rather than processing complete image data. This partial action approach provides sufficient information for reliable auto-return navigation while avoiding the excessive computational and storage resources that would be required for full image processing
Data Source
AI summary
A method of controlling flight of an unmanned aerial vehicle (UAV) includes collecting, while the UAV traverses a flight path, a set of images corresponding to different fields of view of an environment around the UAV using multiple image capture devices, extracting one or more image features from the set of images, constructing a map of the environment using one or more selected image features from the one or more image features, and generating a return path for the UAV using the map of the environment. Each of the multiple image capture devices includes one of the different fields of view.