Single-Camera UAV Photogrammetry With Sensor Fusion Depth Measurement
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Solution Overview
Problem
Current drone-based aerial surveying and inspection methods lack high accuracy and efficiency, particularly in measuring geometric properties of targets, due to limitations in sensor precision and operation complexity.
Innovation Solution
A method involving a drone equipped with an accelerometer sensor, gyro sensor, and camera that captures two aerial images from different positions, using single-camera stereophotogrammetry and sensor fusion algorithms to calculate relative altitude and horizontal distance, thereby deriving pixel depth information and computing geometric properties with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors (barometer, GPS, distance rangers) are used to measure drone altitude, then the measurement coverage is sufficient, but the measurement precision deteriorates to 10-30 feet accuracy
Solution Approach 1:
The patent combines multiple sensors (accelerometer, gyro, barometer, GPS) into an integrated sensor fusion system. The accelerometer and gyro provide high-precision relative motion data, while the barometer and GPS provide absolute position references. By fusing these sensors through Kalman filtering, the system achieves centimeter-level altitude precision without requiring any single sensor to be overly complex or expensive.
Solution Approach 2:
The patent introduces an intermediary computational layer (sensor fusion algorithm) that processes raw sensor data to derive precise altitude measurements. Instead of relying directly on single sensor readings, the system uses accelerometer and gyro data as intermediaries to calculate relative position changes, then combines these with barometric and GPS data to achieve high-precision absolute altitude measurement.
2Measurement precision
If multiple sensors are added to improve measurement precision, then the altitude measurement accuracy improves to centimeter level, but the device complexity increases
Solution Approach 1:
The patent makes the sensor system multi-functional by using the same sensor suite (accelerometer, gyro, barometer, GPS) for multiple purposes: navigation, altitude measurement, and geometric property measurement of target objects. The camera system similarly serves both aerial photography and photogrammetric measurement functions. This universal usage justifies the complexity by extracting maximum value from each component.
Solution Approach 2:
The patent replaces complex mechanical measurement systems with electronic sensor fusion and computational photogrammetry. Instead of using mechanical theodolites or laser rangefinders, the system uses digital sensors and algorithms to achieve precise geometric measurements, reducing mechanical complexity while improving precision.
3Productivity
If conventional aerial surveying methods are used, then the operation process is established, but the productivity deteriorates due to time-consuming operations
Solution Approach 1:
The patent performs preliminary actions by pre-processing sensor data during flight to create orthophotos and extract geometric properties in real-time. The sensor fusion algorithm continuously calculates position and orientation data, and the photogrammetry system pre-computes depth information and measurements during the survey flight itself, rather than requiring extensive post-processing, thereby reducing total operational time.
4Measurement precision
If single-camera stereophotogrammetry is implemented, then the measurement precision reaches centimeter level, but the device complexity increases compared to traditional methods
Solution Approach 1:
The patent replaces complex mechanical stereo camera systems with a single camera combined with computational stereophotogrammetry. By using sensor fusion to accurately determine camera position and orientation, the system can achieve precise 3D measurements from single-camera imagery, substituting mechanical complexity with computational algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables fast, easy, and highly accurate aerial surveying and inspection, with geometric property measurements achievable to the centimeter level, simplifying the process and reducing operational time.
Implementation Method 1
applying new single-camera stereophotogrammetry combined with an accurate sensor fusion algorithm to measure any geometric properties in the captured image
Implementation Method 2
flying a drone with an accelerometer sensor, gyro sensor, and single-camera to capture two aerial images and collect the sensors' data
Implementation Method 3
capturing a first aerial image at a first position with a first altitude; capturing a second aerial image at a second position with a second altitude
Implementation Method 4
It is the intersection of these rays (triangulation) that determines the three-dimensional location of the point
Implementation Method 5
A special case, called stereophotogrammetry, involves estimating the three-dimensional coordinates of points on an object employing measurements made in two or more photographic images taken from different positions
Data Source
AI summary
The disclosure presents novel methods to conduct aerial surveying, inspection and measurements with higher accuracy in a fast and easy way, comprising: (1) flying a drone with an accelerometer, gyro, and camera sensors over a target object; (2) capturing a first aerial image at a first position; (3) capturing a second aerial image at a second position, wherein the second position has a horizontal and vertical displacement from the first position; (4) calculating the displacements between the first and second location using a sensor fusion estimation algorithm from the position sensors' data; (5) solving for the pixel depth information of the aerial images by using a single-camera stereophotogrammetry algorithm with the relative altitude and horizontal distance; (6) deriving the ground sample distance (GSD) of each pixel from the calculated depth information; (7) using the image pixel GSD values to compute any geometric properties of the target objects.