Visual Positioning Reliability Assessment for Mobile Robots
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Solution Overview
Problem
Robots equipped with vision sensors face challenges in evaluating positioning reliability in real-time due to random feature points on visual planes, leading to inefficient and costly manual checks for navigation stability and potential errors.
Innovation Solution
A visual positioning method that extracts corner feature points, classifies them into clusters based on grayscale values and pixel deviations, and selects valid feature points with uniform distribution for improved positioning robustness, using a k-d tree for efficient retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checks are used to verify positioning reliability, then positioning accuracy can be assessed, but the process becomes costly and inefficient
Solution Approach 1:
The system enables robots to automatically evaluate their own positioning reliability by analyzing visual plane features and calculating reliability metrics, eliminating the need for manual inspection and achieving both high precision assessment and operational efficiency
Solution Approach 2:
The system implements real-time feedback mechanisms where positioning reliability is continuously monitored and evaluated based on visual feature analysis, allowing the robot to adjust its navigation based on assessed reliability levels
2Device complexity
If random feature points are used for visual positioning, then the system is simple to implement, but positioning robustness deteriorates in areas with sparse visual features
Solution Approach 1:
The system evaluates positioning reliability locally by analyzing visual plane features in specific regions and calculating reliability metrics for different areas, allowing identification of regions with poor positioning conditions while maintaining overall system simplicity
Solution Approach 2:
The system performs preliminary evaluation of visual plane features and positioning reliability before navigation operations, enabling advance identification of dangerous areas with sparse features and preventing navigation into unreliable zones
3Productivity
If no real-time positioning reliability evaluation is performed, then the system operates smoothly, but navigation errors cannot be detected early
Solution Approach 1:
The system implements real-time feedback by continuously evaluating positioning reliability during navigation operations, providing immediate detection of positioning degradation while maintaining continuous navigation operations
Solution Approach 2:
The system performs preliminary reliability assessment before navigation and continuous monitoring during operation, enabling early detection of positioning issues before they lead to navigation errors
Data Source
AI summary
A visual positioning method and a mobile machine using the same are provided. The method includes: extracting a plurality of corner feature points corresponding to a current image; determining whether a distance between each pair of the plurality of corner feature points is less than a first preset threshold; if yes, determining whether a grayscale value of each of the plurality of corner feature points with the distance less than the first preset threshold is within a second preset threshold range; if yes, obtaining cluster set(s) of the corner feature points; screening a plurality of valid feature points from the cluster set(s); determining a positioning reliability based on a ratio of amount of the valid feature points to an amount of the plurality of corner feature points; and if the positioning reliability is within a preset range, performing a visual positioning based on the positioning reliability.