Indoor Robot Map Validation for Credible Space Updates
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Solution Overview
Problem
Conventional robots often acquire incorrect map data due to errors in location recognition or sensor data, leading to inaccurate updates of indoor space maps.
Innovation Solution
A robot system that compares stored map data with newly acquired data, using distance sensors, travelling history, and line information to determine if errors exist, and updates the map data only when the new data is credible, with user confirmation and external transmission options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the robot continuously updates map data while travelling, then the map data coverage is improved, but the accuracy of map data deteriorates due to location recognition errors or sensor data errors
Solution Approach 1:
The system compares newly acquired map data with previously stored map data to detect inconsistencies and errors. This feedback mechanism allows the robot to identify location recognition errors or sensor data errors by analyzing discrepancies between consecutive map acquisitions, thereby maintaining data accuracy while continuously expanding coverage
Solution Approach 2:
The robot performs error detection and validation checks before finalizing map data updates. By preliminarily comparing new map data with existing data and identifying potential errors in advance, the system prevents inaccurate data from being incorporated into the master map, thus maintaining high accuracy during continuous updates
2Productivity
If the robot performs location recognition and map data acquisition simultaneously, then the operational efficiency is improved, but the reliability of map data deteriorates due to errors in location recognition or sensor data
Solution Approach 1:
The comparison mechanism provides feedback on the quality of simultaneously acquired location and map data. By detecting inconsistencies between new and existing map data, the system can identify errors introduced during simultaneous acquisition, thereby maintaining reliability without sacrificing operational efficiency
Solution Approach 2:
The system performs self-validation by comparing its own newly acquired map data with previously stored data. This self-service error detection allows the robot to maintain high reliability through autonomous quality control while continuing simultaneous location recognition and map acquisition operations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures high credibility of map data, enhancing the quality of services and user convenience by accurately updating map data based on reliable new information.
Implementation Method 1
a distance sensor and a processor configured to, based on acquiring second map data on the basis of distance data acquired by the distance sensor
Data Source
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AI summary
A robot is disclosed. The robot comprises: a memory that stores first map data corresponding to a first region of a specific space; a distance sensor; and a processor. The processor may: if second map data is acquired while the robot is travelling in the specific space, compare the second map data with the first map data; and if it is identified, as the result of the comparison, that there is no error in the second map data and the second map data includes information regarding a second region other than the first region, update the first map data on the basis of the second map data.