Autonomous Mobile Device Map Accuracy via Submap Assessment
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Solution Overview
Problem
Autonomous mobile devices face challenges in maintaining accurate occupancy maps of physical spaces due to sensor limitations, uncertainty, and dynamic environments, leading to inefficient re-exploration and resource consumption.
Innovation Solution
The implementation of navigability and submap assessments allows the device to identify and target areas for re-exploration based on consistency with previous data, reducing unnecessary re-mapping and improving map accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the device performs frequent re-exploration to maintain map accuracy, then map accuracy is improved, but time consumption and power consumption increase
Solution Approach 1:
The system performs partial re-exploration by selectively targeting only those areas of the physical space that have been identified as inconsistent or unreliable in the occupancy map, rather than re-exploring the entire space. This is achieved through assessing navigability to previously visited locations and identifying specific regions that require verification, thereby reducing time consumption while maintaining map accuracy.
Solution Approach 2:
The system applies different exploration strategies to different regions of the physical space based on their individual reliability characteristics. Areas with high reliability are not re-explored, while areas with low reliability or inconsistency are targeted for re-exploration. This localized approach optimizes resource allocation by focusing computational and temporal resources only where needed.
2Measurement precision
If the device performs frequent re-exploration to maintain map accuracy, then map accuracy is improved, but power consumption increases
Solution Approach 1:
The system consumes power partially by limiting re-exploration activities to only those specific areas that require verification, rather than continuously powering up sensors and motors for full-space re-exploration. The assessment module identifies problematic regions, and the device selectively navigates to and explores only those areas, significantly reducing overall power consumption.
Solution Approach 2:
The system uses its own operational data (trajectory data, occupancy map data, navigability assessments) to autonomously identify which areas require re-exploration without external intervention or continuous monitoring. The device self-manages its re-exploration schedule based on internal assessments of map reliability, reducing unnecessary power consumption.
3Measurement precision
If the device performs frequent re-exploration to maintain map accuracy, then map accuracy is improved, but computational resources increase
Solution Approach 1:
The system segments the physical space into multiple regions or areas based on navigability assessments and occupancy map data. Each region is independently evaluated for reliability and consistency. This segmentation allows the computational system to process and assess smaller, manageable portions of the map rather than treating the entire space as a single unit, reducing overall computational complexity.
Solution Approach 2:
The system applies computational assessment and verification processes locally to specific regions that exhibit inconsistency or unreliability, rather than uniformly processing the entire occupancy map. The assessment module identifies problematic local areas and focuses computational resources on verifying only those regions, significantly reducing the total computational burden.
4Stability of the object's composition
If the device performs unnecessary re-exploration, then map coverage is maintained, but device availability for tasks decreases
Solution Approach 1:
The system performs minimal necessary re-exploration by targeting only specific areas that require verification, rather than conducting comprehensive re-mapping of the entire physical space. This partial approach maintains adequate map coverage for operational purposes while freeing the device to perform productive tasks in the meantime, optimizing the balance between map maintenance and task execution.
Data Source
AI summary
An autonomous mobile device (AMD) uses sensors to explore a physical space and determine a map indicating the locations of obstacles. As data is later acquired, the map may be updated, resulting in changes to previously mapped obstacle positions. Navigability tests determine if a path can be found between a current location and a previously visited location. A failure to find a path may result in exploration of an area that includes the previously visited location. Submaps may be produced by the AMD as it moves through the physical space. An accuracy score of a selected submap is determined by comparing the selected submap with a partial global map created using submaps other than the selected submap. If the accuracy score is less than a threshold value, the selected submap may be discarded and an area in the physical space associated with the selected submap may be re-explored.