Horizon Detection for UAVs Using Brightness Line Patterns
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Solution Overview
Problem
Existing autonomy systems in robotics are typically configured to address only one aspect of robot operation, such as automatic control, task allocation, or data processing, limiting their extensibility and efficiency.
Innovation Solution
The development of a system and method for horizon detection in robots, specifically unmanned aerial vehicles (UAVs), which involves acquiring an image, defining a line pattern, searching for an estimated true horizon based on brightness differences, and refining it through iterative searches and edge detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing autonomy systems are configured to address only one aspect of robot operation, then the system design can be focused on a narrow mission set, but the extensibility of the system is limited
Solution Approach 1:
The patent implements a universal autonomy system architecture that can handle multiple aspects of robot operation (automatic control, task allocation, data processing, route planning) through a single integrated framework. The system uses a common software architecture with standardized interfaces and algorithms that can be applied across different mission types and robot configurations, enabling one system to serve multiple functions without requiring separate specialized systems for each aspect.
2Productivity
If existing autonomy systems focus on a narrow mission set, then the underlying algorithms and software architecture can be optimized for specific tasks, but the efficiency and operation of the system in general environments is reduced
Solution Approach 1:
The system employs parameterizable algorithms and configuration-based task allocation that can be adjusted according to different mission requirements. The autonomy framework uses configurable parameters for task priorities, execution thresholds, and coordination behaviors, allowing the same core system to efficiently handle diverse missions by simply changing parameters rather than rewriting algorithms, thus maintaining high efficiency across different application domains.
3Measurement precision
If horizon detection uses simple brightness-based line detection, then the processing speed is fast, but the accuracy of true horizon detection is insufficient
Solution Approach 1:
The horizon detection system performs preliminary image processing steps including brightness thresholding and line pattern definition before the main detection algorithm executes. By pre-processing the image to identify potential horizon candidates and establish reference brightness levels, the system reduces the computational burden during the actual horizon detection phase, enabling accurate detection while maintaining acceptable processing speeds through efficient algorithm execution.
Solution Approach 2:
The detection system incorporates feedback mechanisms where the estimated horizon line is validated against the original image characteristics and adjusted iteratively. The algorithm compares detected horizon positions with expected geometric constraints and refines the result based on feedback from brightness profile analysis and edge detection, progressively improving accuracy while limiting processing time through convergence criteria that prevent unlimited iteration.
Data Source
AI summary
A method is provided for horizon detection. The method includes acquiring an image that depicts a view of an environment, and defining a line pattern of lines that divide the image into respective pairs of image segments. The line pattern is formed of lines that are parallel, or intersecting at a common point of intersection. The method incudes searching the lines of the line pattern to identify one of the lines as an estimated true horizon in the image that divides the image into a respective pair of image segments at a boundary of greatest difference in average brightness between the image segments from among the respective pairs of image segments. The method includes determining true horizon in the image from the estimated true horizon. The method may also include an evaluation of the estimated true horizon or the true horizon as to verify one or more expected characteristics.