UAV Vision-Based Landing Using Takeoff Image Matching
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Solution Overview
Problem
Existing technologies for autonomous UAV landing are unreliable due to GPS measurement errors and the need for pre-acquired templates of visual markers, which can differ from actual appearances, affecting landing accuracy and reliability, especially in environments where GPS signals are weak or unavailable.
Innovation Solution
A computer-implemented method and system that uses an imaging device on the UAV to capture reference images during takeoff, annotated with metadata, and compares them with current images to determine spatial relationships, allowing for precise vision-based landing without relying on GPS sensors or preloaded marker templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS sensors are used for autonomous landing, then positioning capability is provided, but measurement precision deteriorates due to GPS measurement errors
Solution Approach 1:
The patent replaces GPS-based positioning (radio wave-based system) with a vision-based positioning system using imaging devices and image matching algorithms. This substitution eliminates GPS measurement errors by using visual feature matching between current and reference images to determine spatial relationships, thereby improving both reliability and precision for autonomous landing.
Solution Approach 2:
The patent introduces visual markers or natural features in the environment as intermediaries for positioning. Instead of directly using GPS signals, the system captures images of these features, extracts feature points, and matches them with reference images to indirectly determine the UAV's position and orientation, achieving high-precision positioning without GPS.
2Ease of operation
If pre-acquired templates of visual markers are used, then landing guidance is provided, but landing accuracy deteriorates because templates can differ from actual appearances
Solution Approach 1:
The patent performs preliminary action by capturing reference images of the actual landing environment and storing them with associated spatial information before the landing phase. During landing, the current image is compared with these pre-captured reference images (not pre-acquired templates) to determine position, ensuring accuracy by using actual environment data rather than idealized templates.
Solution Approach 2:
The patent creates a visual copy of the actual landing environment by capturing reference images during takeoff or at known positions. These reference images serve as a digital replica of the real-world features, allowing accurate position determination through image matching without relying on simplified or idealized marker templates.
3Measurement precision
If vision-based landing without preloaded templates is used, then landing accuracy is improved by using actual surroundings, but device complexity increases due to image capture and processing requirements
Solution Approach 1:
The patent makes the imaging device multi-functional by using it for both navigation (capturing current images during flight) and positioning (comparing with reference images for landing). This universal use of the same hardware component reduces overall system complexity while maintaining high landing accuracy, as no separate dedicated sensors or markers are required.
Solution Approach 2:
The system performs self-service by using the UAV's own imaging device to capture both reference images (during takeoff or at known positions) and current images (during landing approach). The onboard processor then autonomously performs image matching and position determination, eliminating the need for external infrastructure or complex ground-based support systems.
Data Source
AI summary
A computer-implemented method for controlling an unmanned aerial vehicle (UAV) includes obtaining a first image captured by an imaging device carried by the UAV during a takeoff of the UAV from a target location, obtaining a second image from the imaging device in response to an indication to return to the target location, determining a spatial relationship between the UAV and the target location by comparing the first image and the second image, and controlling the UAV to approach the target location based at least in part on the spatial relationship.