UAV Navigation in Low-Light via GNSS and IMU Fusion
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Solution Overview
Problem
Unmanned aerial vehicles (UAVs) face challenges in navigation, especially in low-light or no-light conditions, due to insufficient information from navigation cameras and unreliable compass readings from ferromagnetic materials.
Innovation Solution
The UAV employs a system that uses Global Navigation Satellite System (GNSS) signals and an Inertial Measurement Unit (IMU) to determine its location, velocity, acceleration, and orientation, allowing it to navigate without relying on cameras or magnetometers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If navigation cameras are used for autonomous navigation, then navigation accuracy is improved under normal lighting conditions, but navigation reliability deteriorates in low-light or no-light environments
Solution Approach 1:
The patent combines multiple navigation systems (visual inertial navigation, magnetic field navigation, and inertial navigation) into a unified navigation system. The processor integrates data from cameras, magnetometers, and inertial measurement units, switching between or combining different navigation methods based on environmental conditions to maintain reliable navigation across all lighting scenarios.
2Measurement precision
If magnetometers or compasses are used for orientation, then heading accuracy is improved, but measurement reliability deteriorates near ferromagnetic materials
Solution Approach 1:
The patent introduces inertial measurement units (accelerometers and gyroscopes) as intermediary sensors that do not rely on magnetic fields. When magnetic interference is detected or anticipated near ferromagnetic structures, the system uses inertial data to determine orientation and heading, bypassing the corrupted magnetic field measurements entirely.
3Reliability
If multiple sensor systems are integrated for all-conditions navigation, then navigation reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic sensor fusion where the processor adaptively selects and weights different navigation data sources based on current environmental conditions. The system transitions between navigation modes (visual-inertial, magnetic, or inertial-only) as needed, optimizing the use of available sensors rather than continuously processing all sensor inputs, thereby managing computational complexity.
Data Source
AI summary
In some examples, an aerial vehicle may determine, based on sensor information received from at least one onboard sensor, that an amount of light fails to satisfy a light threshold. Based at least in part on determining that the amount of light fails to satisfy the light threshold, the aerial vehicle is caused to takeoff at a specified trajectory and a specified acceleration for enabling navigation via an inertial measurement unit (IMU) and a satellite positioning system. Further, the aerial vehicle is directed to navigate an environment based at least on determining a relative heading of the aerial vehicle from information received from the IMU and information received from the satellite positioning system.