Tail Stereo Camera Calibration for Aircraft Ground Collision Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current aircraft ground collision avoidance systems face challenges with camera-based systems due to high retrofit costs and calibration complexities, particularly with mechanical mounting inaccuracies and flexure leading to miscalibration over time, which affect the reliability of stereoscopic and structure from motion methods for obstacle detection.
Innovation Solution
A self-calibrating camera system with power-on calibration using existing feature points on the aircraft's top view, allowing for rigid mounting of tail cameras that auto-calibrate during power-on, and using a marking template to identify camera orientations, enabling efficient and accurate 3D tracking of obstacles using stereoscopic and structure from motion techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cameras are mounted on the aircraft using mechanical mounting, then the camera system can be installed on the aircraft, but mechanical mounting inaccuracies and flexure lead to calibration errors and miscalibration over time
Solution Approach 1:
The system performs self-calibration by automatically detecting feature points on the aircraft structure and computing camera parameters without requiring manual intervention or external calibration equipment. The calibration process is executed autonomously using the aircraft's own visual features as reference
Solution Approach 2:
The calibration is performed in advance during system initialization or maintenance periods, storing the calibrated parameters for use during normal operation. This preliminary calibration ensures accuracy is established before actual collision avoidance operations begin
2Measurement precision
If stereoscopic and structure from motion methods are used for obstacle detection, then 3D tracking of obstacles can be achieved, but the system becomes sensitive to calibration errors and mechanical flexure
Solution Approach 1:
The system continuously updates and adapts calibration parameters based on real-time detection of aircraft structural features, allowing it to compensate for dynamic changes in camera position due to flexure or movement during operation
Solution Approach 2:
The system uses feedback from detected feature points and their expected positions to continuously refine calibration parameters, ensuring that measurement precision is maintained even when mechanical conditions change during aircraft operation
3Area of stationary object
If multiple cameras are mounted on different parts of the aircraft, then broader coverage of the forward and side views can be achieved, but cameras cannot see each other and are not rigidly connected due to flexure
Solution Approach 1:
The system uses visual feature points on the aircraft structure as intermediaries to establish and maintain the spatial relationships between cameras. These feature points serve as reference markers that allow the system to compute relative camera positions even when direct physical connections are absent
Data Source
Figure 1~1A
Figure 2
Figure 3
AI summary
A collision avoidance system comprises a pair of video cameras mounted to a vertical stabilizer of the aircraft, a machine vision processing unit, and a system to inform the pilots of a potential collision. The machine vision processing unit is configured to process image data captured by the video cameras using stereoscopic and structure from motion techniques to detect an obstacle that is near or in the path of the aircraft. Estimates of the range to the object and the rate of change of that range are computed. With the range and range rate, a time to collision can be estimated toward every point of the aircraft. A pilot warning can be sounded based on the nearness of the potential collision. A method of calibrating the video cameras using existing feature points on the top of the aircraft is initiated in response to power being turned on.