UAV Semantic AGL Validation for Cross-Checking Perception Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Unmanned aerial vehicles (UAVs) face challenges in accurately estimating altitude above ground level (AGL) to avoid collisions with ground-based obstacles, particularly in complex environments, as existing systems lack reliable and accurate perception validation mechanisms.
Innovation Solution
A neural network is trained to analyze aerial images from an onboard camera to generate a semantic AGL estimate, which is used to cross-validate and improve the accuracy of perception sensor data from GNSS, IMU, stereovision depth, and lidar sensors, and is combined with other AGL estimates to enhance navigation safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional perception sensors (GNSS, IMU, stereovision, lidar) are used for AGL estimation, then the system has established measurement capabilities, but the accuracy and reliability are insufficient in complex environments
Solution Approach 1:
The patent combines multiple independent AGL estimation methods (semantic AGL from neural network image analysis and non-semantic AGL from traditional sensors like GNSS, IMU, stereovision, and lidar) into a unified perception validation system. This merging allows cross-validation between different measurement approaches, improving both accuracy and reliability by comparing results from diverse sensing modalities.
Solution Approach 2:
The patent introduces a neural network-based semantic AGL estimation system as an intermediary validation layer. This semantic AGL estimate acts as a mediator that cross-validates the non-semantic AGL estimates from traditional sensors, providing an independent verification mechanism that enhances overall system reliability without replacing existing sensing capabilities.
2Reliability
If multiple perception sensors are deployed for AGL estimation, then measurement coverage is improved, but system complexity increases
Solution Approach 1:
The system employs self-service through automated perception validation, where the neural network automatically analyzes aerial images to generate semantic AGL estimates that validate the other sensors. This self-validating mechanism reduces the need for additional complex hardware while maintaining high reliability through software-based cross-validation.
Solution Approach 2:
The patent substitutes mechanical/sensor-based AGL estimation with a neural network-based visual analysis system. Instead of relying solely on physical sensors (GNSS, IMU, lidar), the system uses image processing and semantic analysis to estimate AGL, replacing complex hardware dependencies with intelligent software processing that can validate sensor readings.
3Measurement precision
If semantic AGL estimation via neural network is added, then validation accuracy is improved, but computational requirements increase
Solution Approach 1:
The system applies partial action by using the neural network for validation purposes rather than continuous operation. The semantic AGL estimation is performed to validate sensor readings at critical moments, rather than continuously processing images, thereby reducing computational energy requirements while maintaining validation accuracy when needed.
Data Source
AI summary
A method for perception validation of an unmanned aerial vehicle (UAV) includes: acquiring an aerial image of a ground area with an onboard camera system of the UAV, generating a semantic above ground altitude (AGL) estimate with a neural network trained to output the semantic AGL estimate in response to the aerial image fed as an input to the neural network, generating a motion estimate or a position estimate based upon perception sensor data output from a perception sensor disposed onboard the UAV, and cross-validating the motion or position estimate against the semantic AGL estimate.