UAV Sensor Targeting Accuracy via Pose Estimation and Kalman Filtering
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Solution Overview
Problem
Unmanned aerial vehicles (UAVs) face challenges in maintaining accurate target aiming due to errors in flight control and sensor orientation, especially in complex and dynamically changing environments, which affects the effectiveness of industrial asset inspections.
Innovation Solution
A system that includes a position and orientation measuring unit, a pose estimation platform, and a geometry evaluation platform to calculate and improve target aiming accuracy using a first-order model and extended Kalman filters, along with image feature extraction and three-dimensional model data to adjust transformations and compute targeting points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If flight control and sensor orientation are used in UAV inspection, then automated inspection capability is improved, but target aiming accuracy deteriorates due to control errors and sensor noise
Solution Approach 1:
The system implements feedback by continuously measuring actual UAV position and sensor orientation using position and orientation measuring units, comparing these measurements with planned positions and orientations, and using the calculated aiming errors to adjust and correct the targeting accuracy in real-time during the inspection process
Solution Approach 2:
The patent replaces direct mechanical precision control with a computational approach, using mathematical models (first-order model, transfer functions) and algorithms (extended Kalman filters) to calculate and compensate for aiming errors, substituting mechanical precision requirements with software-based correction
2Measurement precision
If multiple sensors and data sources are integrated to improve aiming accuracy, then target aiming accuracy is improved, but device complexity increases
Solution Approach 1:
The system merges multiple data sources including position measurements, orientation measurements, three-dimensional model data, and image feature extraction into a unified aiming error calculation framework, integrating these diverse inputs through extended Kalman filters to produce a comprehensive correction
3Measurement precision
If extended Kalman filters and multiple data sources are used to calculate aiming error, then target aiming accuracy is improved, but loss of time in processing increases
Solution Approach 1:
The system performs preliminary action by pre-loading three-dimensional model data of the industrial asset and pre-establishing the first-order model and transfer functions before the inspection flight, so that during the actual inspection, only real-time position and orientation measurements need to be processed, significantly reducing on-the-fly computation time
Data Source
AI summary
System and methods may evaluate and/or improve target aiming accuracy for a sensor of an Unmanned Aerial Vehicle (“UAV”). According to some embodiments, a position and orientation measuring unit may measure a position and orientation associated with the sensor. A pose estimation platform may execute a first order calculation using the measured position and orientation as the actual position and orientation to create a first order model. A geometry evaluation platform may receive planned sensor position and orientation from a targeting goal data store and calculate a standard deviation for a target aiming error utilizing: (i) location and geometry information associated with the industrial asset, (ii) a known relationship between the sensor and a center-of-gravity of the UAV, (iii) the first order model as a transfer function, and (iv) an assumption that the position and orientation of the sensor have Gaussian-distributed noises with zero mean and a pre-determined standard deviation.