UAV Backup Navigation Using Visual Feature Maps After GPS Failure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Unmanned aerial vehicles (UAVs) face navigation failures due to primary navigation system malfunctions or spoofing, leading to potential crashes or loss of control, and existing backup navigation systems like Visual Odometry and Simultaneous Localization And Mapping algorithms are inadequate for real-time, reliable navigation.
Innovation Solution
A backup navigation system that generates and updates a map using environmental imagery during flight, transitioning to a feature localization mode to navigate the UAV to a safe landing zone upon primary navigation failure, and utilizes spatial and temporal maps from a database for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a backup navigation system is implemented to replace primary navigation upon failure, then reliability is improved, but device complexity increases
Solution Approach 1:
The backup navigation system performs preliminary actions by capturing images and localizing visual features along the flight path before primary navigation failure occurs. This pre-mapping approach ensures that navigation data is already prepared and stored, enabling immediate transition to backup mode without adding complex real-time processing systems.
Solution Approach 2:
The backup navigation system creates a copy of the flight path information through visual feature localization and map generation. Instead of relying on the primary navigation system's data structures, it independently captures and stores visual landmarks and their positions, providing a redundant navigation dataset that can be used when the primary system fails.
2Speed
If visual feature localization is performed in real-time during flight, then navigation speed is improved, but measurement precision deteriorates
Solution Approach 1:
Visual features are localized and mapped during the outbound flight before any failure occurs. This preliminary localization ensures that feature positions are accurately recorded with precise coordinate data stored in the backup system, eliminating the need for rushed real-time analysis during emergency situations.
Solution Approach 2:
The backup navigation system continuously compares the stored map of visual features with current camera observations to determine UAV position. This feedback mechanism provides ongoing location updates with consistent precision, allowing the system to maintain accurate navigation without requiring high-speed real-time processing during critical moments.
3Adaptability or versatility
If the backup navigation system uses generated maps during flight, then adaptability is improved, but loss of information increases
Solution Approach 1:
The system creates redundant copies of navigation information through dual mapping: the primary navigation system maintains its own data structures while the backup system independently generates and stores visual feature maps. This duplication ensures that if the primary system's data is corrupted or lost, the backup copy remains intact and can be immediately activated.
Solution Approach 2:
The backup navigation system uses different data representation parameters than traditional navigation systems. Instead of relying on GPS coordinates and pre-programmed waypoints, it stores visual feature descriptors, image data, and relative position information. This parameter diversity allows the system to adapt to various failure modes and environmental conditions while preserving navigation capability.
Data Source
AI summary
Described is a method that involves operating an unmanned aerial vehicle (UAV) to begin a flight, where the UAV relies on a navigation system to navigate to a destination. During the flight, the method involves operating a camera to capture images of the UAV's environment, and analyzing the images to detect features in the environment. The method also involves establishing a correlation between features detected in different images, and using location information from the navigation system to localize a feature detected in different images. Further, the method involves generating a flight log that includes the localized feature. Also, the method involves detecting a failure involving the navigation system, and responsively operating the camera to capture a post-failure image. The method also involves identifying one or more features in the post-failure image, and determining a location of the UAV based on a relationship between an identified feature and a localized feature.