Mobile Robot Pose Determination Using Global and Local Map Matching
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Solution Overview
Problem
Existing positioning systems for mobile robots in complex environments face challenges in accurately determining the position and posture of mobile subjects due to the complexity of the environment and the need for precise mapping and matching of sensor data with reference maps.
Innovation Solution
A method and system that utilize odometer data, laser radar data, and reference maps to determine a target pose of a mobile subject by reconstructing sub-maps and matching them with laser data, incorporating both global and local matching results to improve positioning accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser radar data is matched with reference map for positioning, then positioning accuracy is improved, but system complexity increases due to map reconstruction and multiple matching operations
Solution Approach 1:
The positioning system divides the matching process into two independent modules: global matching (laser data with reference map) and local matching (sub-map with laser data). This segmentation allows each module to specialize in different spatial scales, improving overall positioning accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system merges multiple data sources (odometer data, global matching results, local matching results) through data fusion to determine the final target pose. This combination leverages the strengths of each data source: odometer provides continuous motion information, global matching provides absolute position reference, and local matching provides precise local positioning, collectively achieving high accuracy positioning.
2Reliability
If multiple data sources are fused for pose determination, then positioning robustness is improved, but computational load increases
Solution Approach 1:
The system performs preliminary actions by pre-constructing the reference map and sub-maps before actual positioning operations. This preparation work organizes spatial data in advance, allowing the matching algorithms to operate more efficiently during real-time positioning, thus reducing computational load while maintaining robust multi-source data fusion.
3Measurement precision
If sub-map reconstruction is performed for local matching, then local positioning accuracy is improved, but processing time increases
Solution Approach 1:
The system applies local quality by constructing a sub-map that specifically represents the local environment around the mobile robot with high detail. This sub-map focuses computational resources on the relevant local area rather than processing the entire reference map, achieving high local positioning accuracy while reducing overall processing time through selective detail representation.
Data Source
AI summary
Some embodiments of the present disclosure provide methods and systems for pose determination of a mobile subject. The method may include obtaining odometer data acquired by an odometer of a mobile subject at a current time, laser data of a scene around the mobile subject acquired, at the current time, by a laser radar of the mobile subject, and a reference map of a region where the scene is located. The method may also include determining a first matching result based on the reference map and the laser data. The method may also include reconstructing a sub map reflecting the scene based on the laser data and determining a second matching result based on the sub map and the laser data. The method may further include determining a target pose of the mobile subject based on at least two of the odometer data, the first matching result, or the second matching result.