Robot Map Generation With Morphological Obstacle Contour Correction
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Solution Overview
Problem
Existing methods for generating robot maps are inefficient due to high noise levels and inaccurate obstacle detection, requiring significant time, cost, and effort for manual correction.
Innovation Solution
A method and system that automatically identify and correct noise and obstacle contours in robot maps by performing dilation and erosion operations on pixels estimated to be obstacles, using color and size criteria to determine polygon-based contours.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual modification is performed to remove noises and correct obstacle contours, then map accuracy is improved, but time consumption and labor cost increase significantly
Solution Approach 1:
The system performs automatic noise removal and obstacle contour correction using image processing algorithms, enabling the map generation system to self-correct without human intervention. The processor automatically identifies and removes fine noises, corrects blurry contours, and annotates obstacle contours, replacing manual modification processes entirely
Solution Approach 2:
The patent replaces manual mechanical editing processes with automated image processing and computer vision algorithms. The system uses pixel analysis, edge detection, and contour recognition algorithms to automatically identify and correct map errors, substituting human visual inspection and manual editing with computational methods
2Productivity
If raw map is used as it is without modification, then processing time is reduced, but map quality deteriorates with considerable noises and inaccurate obstacle contours
Solution Approach 1:
The system performs noise removal and contour correction operations automatically during the map generation process itself, rather than requiring separate post-processing steps. The image processing algorithms are integrated into the workflow, so corrections are made preliminarily before the map is finalized and used by the robot
Solution Approach 2:
The automated image processing operates continuously during map generation, maintaining constant refinement of the map data. The system processes the raw map through multiple filtering and correction stages in sequence, ensuring that useful actions (noise removal, contour correction) are performed continuously without interruption or manual intervention
3Measurement precision
If manual annotation of obstacle contours is performed, then contour accuracy is improved, but labor cost and effort increase significantly
Solution Approach 1:
The patent replaces manual annotation of obstacle contours with automated image processing algorithms that detect edges, identify object boundaries, and generate polygon-based contour representations. The system uses computer vision techniques to automatically annotate contours without human labor
Solution Approach 2:
The system creates an automated computational copy of the manual contour annotation process through image processing algorithms. These algorithms replicate the function of human contour detection and annotation by analyzing image gradients, edges, and object boundaries to generate accurate contour representations automatically
Data Source
AI summary
According to one aspect of the invention, there is provided a method for generating a map for a robot, the method comprising the steps of: acquiring a raw map associated with a task of the robot; identifying pixels estimated to be a moving obstacle in the raw map, on the basis of at least one of colors of pixels specified in the raw map and sizes of areas associated with the pixels; and performing dilation and erosion operations on the pixels estimated to be the moving obstacle, and determining a polygon-based contour of the moving obstacle.