Historical map utilization method based on vision robot
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Solution Overview
Problem
Vision robots frequently rebuild or update maps during traversal, leading to inefficiencies and mismatches between historical and current maps, causing reduced working efficiency and inaccurate positioning.
Innovation Solution
A method where a vision robot independently processes road sign information from both current and historical maps using matching algorithms to calculate transformation relationships, allowing the historical map to be updated and used for motion control, ensuring accurate path planning and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot rebuilds or updates maps frequently during traversal, then the map accuracy is improved, but the working efficiency deteriorates and the historical map becomes invalid
Solution Approach 1:
The system performs preliminary actions by collecting and processing road sign information from both current and historical maps before actual navigation. The transformation relationship between coordinate systems is pre-calculated, allowing the historical map to be proactively adapted to the current environment, thus maintaining its validity without requiring frequent complete map rebuilds.
Solution Approach 2:
The system changes parameters by transforming the coordinate system of the historical map to match the current map's coordinate system. By calculating transformation relationships (rotation angles, translation vectors) between the two coordinate systems, the historical map data is adjusted to remain accurate and useful without needing complete reconstruction.
2Reliability
If the map is updated to generate a new map, then the map freshness is improved, but the historical map information is lost and task continuity fails
Solution Approach 1:
The system merges historical map information with current map data by establishing transformation relationships between their coordinate systems. Instead of replacing historical maps with new ones, it combines them through coordinate transformation, allowing both historical and current information to coexist and be used together for navigation and task continuity.
Solution Approach 2:
The system creates a transformed copy of the historical map in the current coordinate system. By copying historical map data and applying coordinate transformation, the historical information is preserved and adapted to the current environment, enabling continuous use of historical navigation data without information loss.
3Adaptability or versatility
If the vision robot changes physical position and motion direction between tasks, then the robot flexibility is improved, but the historical map positioning accuracy deteriorates
Solution Approach 1:
The system uses feedback by continuously collecting road sign information from the current environment and comparing it with historical map data. The transformation relationship between coordinate systems is dynamically calculated based on this feedback, allowing the system to compensate for changes in robot position and orientation, thus maintaining positioning accuracy despite increased flexibility.
Solution Approach 2:
The coordinate transformation relationship acts as an intermediary between the historical map coordinate system and the current map coordinate system. This intermediary transformation layer allows the system to bridge the gap caused by robot position and orientation changes, enabling accurate positioning by translating coordinates between the two reference frames.
4Adaptability or versatility
If the vision sensor collects images at different poses, then the robot adaptability is improved, but the map matching accuracy deteriorates
Solution Approach 1:
The system replaces direct mechanical/physical matching of map images by using computational coordinate transformation. Instead of relying on exact pose matching during image collection, it substitutes the matching process with mathematical transformation of coordinate systems, allowing accurate map matching even when images are collected at different poses.
Data Source
AI summary
The disclosure discloses a historical map utilization method based on a vision robot, including: S1, the vision robot is controlled to continuously collect image of a preset road sign of a preset working area, and road sign information of the preset road sign is obtained; S2, the obtained road sign information is transmitted to a first map positioning system and a second map positioning system; S3, the first map positioning system and the second map positioning system are controlled to respectively process corresponding road sign information, thereby obtaining first pose information and second pose information; S4, the first pose information and the second pose information under the same preset road sign are selected, and a transformation relationship formula of the first pose information and the second pose information is calculated; and S5, the historical map is controlled to perform a corresponding transformation operation, and subsequent motion control is performed.