Sensor Fusion for UAV State Estimation Using Iterative Optimization
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Solution Overview
Problem
Existing approaches for data collection and processing in unmanned aerial vehicles (UAVs) are not optimal, particularly in terms of accuracy for estimating state information, which can impact UAV functionality.
Innovation Solution
The use of sensor fusion techniques combining inertial sensors and image sensors through an iterative optimization algorithm to determine updated state information, including position, orientation, and velocity, for enhanced accuracy and flexibility in UAV operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor fusion techniques combining inertial sensors and image sensors are used, then measurement precision of state information is improved, but device complexity increases
Solution Approach 1:
The patent combines inertial sensors (accelerometers, gyroscopes) with image sensors (cameras) into an integrated sensor fusion system. The inertial measurement unit (IMU) and camera system work together through coordinate transformation and data fusion algorithms to jointly estimate UAV state information, thereby improving measurement precision while managing system complexity through unified processing architecture
Solution Approach 2:
The sensor fusion system performs multiple functions simultaneously: inertial sensors provide motion detection and orientation data, image sensors capture visual environment information and verify position, and the integrated system enables both navigation and obstacle detection capabilities, achieving multi-functionality that improves overall measurement accuracy without requiring separate dedicated systems for each function
2Measurement precision
If iterative optimization algorithms are used for state estimation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary coordinate transformations between inertial and image sensor reference frames before executing the main iterative optimization algorithm. By pre-processing sensor data and establishing initial state estimates through coordinate alignment, the system reduces the computational burden during real-time optimization, thereby improving measurement precision while minimizing additional computation time
Solution Approach 2:
The iterative optimization algorithm continuously processes sensor fusion data in real-time during UAV operation, maintaining continuous state estimation rather than performing batch processing. This continuous action allows the system to achieve high measurement precision through ongoing optimization while keeping computation time distributed across operational periods, avoiding large time losses at specific moments
Data Source
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AI summary
Systems, methods, and devices are provided for controlling a movable object using multiple sensors. In one aspect, a method for estimating state information for a movable object is provided. The method can comprise: receiving previous state information for the movable object; receiving inertial data from at least one inertial sensor carried by the movable object; receiving image data from at least two image sensors carried by the movable object; and determining updated state information for the movable object based on the previous state information, the inertial data, and the image data using an iterative optimization algorithm during the operation of the movable object.