Aerial Navigation Image Matching Under GNSS Denial and Low Light
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current navigation systems for vehicles, especially aerial vehicles, face challenges in reliability and accuracy when GNSS signals are disrupted, and image-based navigation is problematic in low light conditions, leading to increased uncertainty over time.
Innovation Solution
A method that uses a sensor image and a database of three-dimensional geo-referenced information to determine the vehicle's pitch angle, roll angle, and position by comparing sensor images with two-dimensional perspective view images, utilizing both low ambient light and daylight texture data to maintain navigation accuracy regardless of ambient light conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image-based navigation is used under low ambient light conditions, then navigation can be maintained, but matching accuracy deteriorates due to insufficient light information
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor images to enhance their quality before matching. This includes applying noise reduction filters, contrast enhancement, and other image processing techniques to improve the matchability of low-light sensor images against the database, thereby resolving the contradiction between maintaining navigation reliability and preserving matching accuracy in low-light conditions
2Measurement precision
If GNSS is used for navigation, then position accuracy is maintained, but navigation fails when GNSS signals are denied or interrupted
Solution Approach 1:
The system changes the operational parameters by switching between different navigation modes depending on signal availability. When GNSS signals are available, the system uses GNSS-based navigation for high precision. When GNSS signals are denied or interrupted, the system transitions to image-based navigation using the database, thereby maintaining navigation availability while accepting a change in precision parameters
3Reliability
If inertial measurements units are used for dead reckoning, then navigation continues without external signals, but position uncertainty increases over time
Solution Approach 1:
The system implements feedback mechanisms by continuously comparing sensor images with corresponding images from the database to detect and correct drift accumulation. The database serves as a reference framework that provides feedback on the vehicle's actual position and orientation, allowing the system to correct errors that would otherwise accumulate over time in dead reckoning navigation
Data Source
AI summary
The present disclosure relates to a method and system for navigation of an aerial vehicle. The method (100) comprises providing (110) a sensor image from an aerial vehicle sensor and repeatedly, until at least one predetermined criterion is reached, performing the steps: setting (120) input data comprising information related to pitch angle, roll angle, yaw angle and three-dimensional position of the aerial vehicle; providing (130) at least one two-dimensional perspective view image based on the input data, where the at least one two-dimensional perspective view image is obtained from a database comprising three-dimensional geo-referenced information of the environment, said three-dimensional geo-referenced information comprising texture data; and comparing (140) the sensor image and the at least one two-dimensional perspective view image.