Multi-Sensor Image Selection for Low-Latency UAV Navigation
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Solution Overview
Problem
Existing navigation systems for unmanned aerial vehicles (UAVs) face challenges with poor quality sensor data from single sensors and increased processing delays when using data from multiple sensors, which can impair UAV functionality.
Innovation Solution
A navigation system that employs multiple imaging devices to capture images from different fields of view, with processors calculating feature points to select the highest quality image data for processing, thereby improving state information assessment while conserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple sensors are processed to assess state information, then the accuracy of state information is improved, but the processing delay of the system is increased
Solution Approach 1:
The patent segments the sensor data processing by dividing images into multiple blocks and processing each block independently to detect feature points. This segmentation allows parallel processing of different image regions, improving computational efficiency and reducing overall processing delay while maintaining accurate state information assessment through aggregated feature points from all blocks.
Solution Approach 2:
The patent implements partial action by selectively processing only certain image blocks based on their quality metrics. Instead of uniformly processing all sensor data from multiple sensors, the system identifies and processes only the high-quality image blocks that contribute most to accurate state assessment, thereby reducing unnecessary computation and processing delay while maintaining measurement precision.
2Loss of time
If sensor data from a single sensor is used to assess state information, then the processing delay is reduced, but the quality of sensor data is poor
Solution Approach 1:
The patent merges data from multiple sensors by combining feature points detected from multiple image sources. The system processes images from multiple sensors, extracts feature points from each, and integrates them to assess state information. This merging approach maintains fast processing by using efficient block-based methods while improving data quality through the combined information from multiple sensors.
Solution Approach 2:
The patent changes the parameter of image quality assessment by introducing feature point quality metrics and using these metrics to selectively process image blocks. The system evaluates parameters such as feature point density and distribution quality to determine which image blocks from multiple sensors should be processed, thereby optimizing the balance between processing speed and data quality.
3Measurement precision
If all imaging device data is processed to assess state information, then the accuracy is improved, but computing resources are consumed
Solution Approach 1:
The patent extracts only the essential and high-quality feature points from image blocks for state information assessment. Instead of processing all imaging device data in full detail, the system extracts key feature points from selected image blocks based on quality metrics, thereby reducing computing resource consumption while maintaining the accuracy needed for reliable state assessment.
Solution Approach 2:
The patent applies partial action by processing only a subset of image blocks that meet quality criteria. The system evaluates each image block and selectively processes only those with sufficient quality metrics, avoiding the computational expense of processing all imaging device data equally. This partial processing approach conserves computing resources while maintaining measurement precision through selective inclusion of high-quality data.
Data Source
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AI summary
Systems and methods for navigating a vehicle within an environment are provided. In one aspect, a method comprises: (a) selecting, with aid of a processor, a subset of a plurality of sensors to be used for navigating the vehicle within the environment based on one or more predetermined criteria, wherein the plurality of sensors are arranged on the vehicle such that each sensor of the plurality of sensors is configured to obtain sensor data from a different field of view; (b) processing, with aid of the processor, the sensor data from the selected sensor(s) so as to generate navigation information for navigating the vehicle within the environment; and (c) outputting, with aid of the processor, signals for controlling the vehicle based on the navigation information.