Monocular Fiducial Marker Distance and Angle Estimation
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Solution Overview
Problem
Existing systems face challenges in accurately estimating object distance and angle using dynamic monocular cameras due to loss of region of interest and high computational demands for calculating yaw and roll angles.
Innovation Solution
A system and method that utilizes a calibration marker with known dimensions to determine calibration parameters, enabling precise estimation of object distance and angle by extracting features from images captured by an image capture device, including the use of fiducial markers like AprilTags for yaw and roll angle calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems calculate yaw and roll angles using traditional methods, then angle estimation is achieved, but processing resources and time are significantly consumed
Solution Approach 1:
The system performs preliminary calibration by capturing images of a calibration marker at multiple known distances and pre-calculating distance-based parameters. This pre-computed calibration data is stored and reused during actual object detection, eliminating the need for repeated complex calculations and significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The system pre-calculates yaw and roll angles during the calibration phase using known geometric relationships and stores these as lookup values. During runtime, the system retrieves pre-computed angle values based on detected marker characteristics, avoiding real-time trigonometric calculations and reducing computational burden.
2Ease of operation
If a dynamic monocular camera is used for image capture, then mobility and flexibility are improved, but region of interest is lost due to distance between camera and objects
Solution Approach 1:
The system introduces a calibration marker as an intermediary object with known dimensions and distinctive visual characteristics. This marker serves as a reference that enables the system to infer distance and spatial relationships even when the camera is far from the target object, compensating for the monocular camera's inability to directly measure depth.
Solution Approach 2:
The system changes the parameter of reference object size by using a calibration marker with known, standardized dimensions. By detecting the apparent size of this known object in the image and comparing it to its actual dimensions, the system can calculate distance and recover spatial information that would otherwise be lost in monocular imaging.
3Measurement precision
If calibration marker with known dimensions is used, then distance estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The system uses a simple, inexpensive calibration marker that can be easily printed or manufactured with basic tools. The marker consists of geometric shapes with known dimensions that can be created using standard printing or cutting methods, avoiding the need for complex, expensive calibration equipment while achieving high measurement precision.
Data Source
AI summary
A system and method for estimating object distance and/or angle from an image capture device may obtain an image including a fiducial marker captured by an image capture device; extract a pixel width and/or a pixel height of the fiducial marker in the image space of the image; determine, based on the pixel width and/or the pixel height, a known width and/or a known height, and one or more calibration parameters, a distance of the fiducial marker from the image capture device; estimate, based on the pixel width, the pixel height, the known width, and the known height, a yaw angle of the fiducial marker with respect to the image capture device; determine, based on a slope of a positional vector of the fiducial marker, a roll angle of the fiducial marker with respect to the image capture device; and/or provide the distance, the yaw angle, and/or the roll angle.