Robotic Map Divergence Detection Using Localized Quality Scores
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Solution Overview
Problem
Robotic systems face challenges in accurately detecting and correcting diverged computer-readable maps, which can lead to navigation errors and collisions due to sensor imperfections, odometry drift, and feature-poor environments.
Innovation Solution
A method and system for detecting diverged maps using scoring metrics such as footprint score, scan consistency score, and scan alignment score, calculated by a processor to assess map quality and correct errors, involving footprint superimposition, scan simulation, and alignment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robotic systems use sensors to collect data during navigation, then map data can be gathered, but sensor imperfections cause map divergence and navigation errors
Solution Approach 1:
The system implements feedback by calculating scoring metrics (footprint score, scan consistency score, scan alignment score) that evaluate map quality and provide information about map divergence. This feedback loop enables the robotic system to detect and correct errors in the computer-readable map, thereby improving navigation reliability despite sensor imperfections.
Solution Approach 2:
The patent introduces scoring metrics as an intermediary between raw sensor data and the final map representation. These metrics (footprint score, scan consistency score, scan alignment score) act as mediators that evaluate the quality of map data and identify divergences, allowing the system to detect and correct errors without being directly affected by sensor imperfections.
2Measurement precision
If the system calculates multiple scoring metrics to detect map divergence, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the map quality assessment into three distinct scoring metrics: footprint score (evaluating robot position consistency), scan consistency score (evaluating sensor data consistency), and scan alignment score (evaluating map feature alignment). This segmentation allows for comprehensive map divergence detection while organizing computational tasks into manageable, specialized components that can be processed efficiently.
3Reliability
If the system corrects diverged maps using scoring metrics, then navigation accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by calculating scoring metrics continuously during navigation to detect map divergence early. By identifying issues before they significantly impact navigation, the system can correct minor divergences more efficiently rather than dealing with accumulated errors, thereby reducing overall processing time while maintaining navigation accuracy.
Data Source
AI summary
Systems and methods for detecting and correcting diverged maps for robotic devices include three scoring metrics that quantify map quality using different methods and properties of the map. The scoring metrics provide localized map quality measurements useful for determining diverged portions of the maps and provide metrics useful for correcting the maps.