Drone Visual Navigation Using QR Features in GPS-Denied Spaces
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Solution Overview
Problem
Conventional drone navigation technologies rely heavily on GPS and compass signals, which are unreliable or unavailable in indoor environments, making them unsuitable for navigation within non-GPS environments such as industrial facilities or other indoor settings.
Innovation Solution
An image-driven self-navigation system for drones that uses visual features like QR codes or bar codes positioned throughout the environment, where processors on the drone receive images, detect these features, determine the drone's location, and convey commands to guide it along a desired route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS and compass signals are used for drone navigation, then navigation reliability is improved in outdoor environments, but navigation becomes unavailable or unreliable in indoor environments
Solution Approach 1:
The navigation system is segmented into multiple independent subsystems: GPS/compass for outdoor navigation and visual feature-based navigation for indoor environments. Each subsystem operates independently in its suitable environment, with the system switching between them based on GPS availability. This segmentation allows the drone to maintain high reliability in each specific environment while achieving overall environmental versatility.
Solution Approach 2:
Visual features (QR codes, bar codes, or natural features) serve as an intermediary navigation reference system that bridges the gap when GPS signals are unavailable. The image processing system acts as a mediator that translates visual information from the environment into navigational commands, enabling the drone to navigate indoors without direct GPS connectivity while maintaining navigation reliability.
2Measurement precision
If visual features like QR codes are positioned throughout the environment for navigation, then navigation accuracy in non-GPS environments is improved, but system complexity increases
Solution Approach 1:
Instead of using complex natural feature recognition systems, the patent employs standardized visual codes (QR codes, bar codes) that contain encoded navigation information. These codes are simplified copies of navigation data that can be quickly decoded by the image processing system, providing high navigation accuracy while minimizing computational complexity. The codes serve as standardized templates that reduce the complexity of environmental interpretation.
Solution Approach 2:
The system changes the parameter of navigation reference from continuous natural features to discrete coded features with specific visual parameters. By encoding navigation information in standardized visual patterns with defined geometric and color parameters, the system simplifies image processing and feature detection algorithms, thereby reducing overall system complexity while maintaining or improving navigation precision.
3Adaptability or versatility
If image processing is used to detect visual features for navigation, then navigation capability in indoor environments is enabled, but processing time and computational load increase
Solution Approach 1:
Visual navigation features (QR codes, bar codes) are pre-positioned throughout the indoor environment before the drone arrives. The drone's image processing system is pre-configured with knowledge of these feature locations and their expected visual characteristics. This preliminary preparation allows the drone to quickly acquire and process visual features upon entering the environment, minimizing real-time processing time while enabling immediate indoor navigation capability.
Data Source
AI summary
Various embodiments relate to unmanned vehicle navigation. A navigation system may include one or more processors configured to communicatively couple with an unmanned vehicle. The one or more processors may be configured to receive an image from the unmanned vehicle and detect a feature within the image. The one or more processors may be further be configured to determine a location of the unmanned vehicle based on the feature and convey one or more commands to the unmanned vehicle based on the location of the unmanned vehicle. Associated methods and computer-readable medium are also disclosed.