Region Outline Map Updating for Robot Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
SLAM mapping and localization technologies in mobile robots, such as floor cleaning robots, often result in construction errors due to complex and dynamic home environments, leading to inaccurate localization and task failure.
Innovation Solution
A method and device for updating region outline maps using laser radar data, where multiple algorithms process distance data to generate and compare outline maps, replacing maps based on similarity to ensure accurate representation of the environment, thereby alleviating inaccurate localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SLAM mapping technology is used in complex and dynamic home environments, then the robot can perform localization and mapping functions, but construction errors occur leading to inaccurate localization
Solution Approach 1:
The patent introduces an intermediary verification mechanism that compares newly constructed map features with historical map data. This intermediary comparison process identifies and corrects construction errors by detecting inconsistencies between current sensor data and previously established environmental features, thereby improving localization accuracy without requiring complete system redesign
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously compares real-time laser radar data with historical map information. When discrepancies are detected, the system generates correction signals that adjust the localization parameters, creating a closed-loop control system that progressively improves mapping accuracy in dynamic environments
2Measurement precision
If multiple algorithms are used to update region outline maps, then the accuracy of environment representation is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by selectively updating only the region outline map using specific algorithms when necessary, rather than continuously applying all processing algorithms. The system determines when updates are needed based on environmental change detection, performing partial map renewals that maintain accuracy while reducing unnecessary computational overhead
Solution Approach 2:
The patent segments the map processing into distinct functional modules: region outline extraction, similarity comparison, and selective updating. By dividing the overall mapping process into independent segments that can be executed selectively based on environmental conditions, the system achieves high accuracy representation while minimizing processing time through intelligent task scheduling
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
Enables real-time updating of region outline maps, improving localization accuracy and ensuring that floor cleaning robots perform tasks correctly in dynamic environments.
Implementation Method 1
acquiring and processing first distance data obtained by a laser radar to generate a first to-be-matched outline map
Data Source
Figure 1~2
Figure 3~5
AI summary
A method and device for drawing an outline of a region and a computer-readable storage medium are provided. The method includes: updating an original region outline map according to a first predetermined algorithm to obtain a first region outline map; updating the original region outline map according to a second predetermined algorithm to obtain a second region outline map; acquiring and processing first distance data obtained by a laser radar to generate a first to-be-matched outline map; comparing the first to-be-matched outline map with the first region outline map and the second region outline map, respectively; and replacing the second region outline map with the first region outline map if the similarity between the first region outline map and the first to-be-matched outline map is higher than the similarity between the second region outline map and the first to-be-matched outline map.