Global Map Regeneration for Dynamic AMR Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous mobile robots (AMRs) face challenges in maintaining accurate maps of dynamic environments, leading to inaccurate localization and potential harm due to outdated maps, as conventional SLAM techniques struggle to update maps and localize simultaneously, especially in changing conditions.
Innovation Solution
An improved mapping and localization system utilizing a map manager that collects and evaluates map data from multiple AMRs, verifying accuracy before updating a global map, allowing real-time updates and integration of map information from different viewpoints, and ensuring map integrity by rejecting inaccurate data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the AMR uses conventional SLAM techniques to update maps and localize simultaneously, then the system complexity is reduced and operation is simplified, but the map accuracy deteriorates and localization precision worsens in dynamically changing environments
Solution Approach 1:
The system separates mapping and localization into independent modules. The map manager handles map updates by collecting data from multiple AMRs and determining quality metrics, while the localization module uses the maintained global map for position determination. This segmentation allows each module to optimize for its specific function, improving map accuracy without increasing overall system complexity.
Solution Approach 2:
The map manager acts as an intermediary between multiple AMRs and the global map. It collects map data from multiple sources, determines quality metrics, and maintains the global map before distributing it to AMRs for localization. This intermediary layer ensures map accuracy by filtering and validating data before it's used for localization purposes.
2Measurement precision
If the AMR operates in mapping mode to collect updated map information, then the map accuracy is improved, but the productivity decreases due to mode switching and time spent away from navigation tasks
Solution Approach 1:
The system merges mapping and localization operations by allowing multiple AMRs to simultaneously contribute map data while maintaining navigation tasks. The map manager aggregates data from multiple sources in real-time, eliminating the need for individual AMRs to switch to dedicated mapping mode. This combining approach maintains map accuracy while AMRs continue their navigation productivity.
Solution Approach 2:
The map manager continuously collects and processes map data from multiple AMRs in real-time, maintaining an updated global map without requiring AMRs to pause their navigation tasks. This continuous operation ensures map accuracy is maintained while productivity is sustained, as data collection occurs concurrently with navigation activities.
3Device complexity
If the AMR relies on a single map source for localization, then the device complexity is reduced, but the reliability deteriorates due to single-point failures and outdated map information
Solution Approach 1:
The system implements local quality assessment by having the map manager determine quality metrics for map data from different AMRs and sources. Each map data contribution is evaluated individually based on its quality, and the global map is maintained using high-quality data. This local quality control ensures reliability without requiring complex centralized management of all possible failure modes.
4Ease of operation
If the AMR uses motion sensors for position estimation in navigation mode, then the ease of operation is improved and energy use is reduced, but the measurement precision deteriorates leading to inaccurate localization
Solution Approach 1:
The system creates a high-precision copy of the environment through the global map maintained by the map manager, which is then used for accurate localization. AMRs continue to use simple motion sensors for navigation, but their position estimates are refined by comparing sensor data against the accurate global map copy. This allows ease of operation with simple sensors while achieving high precision through the map-based reference.
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
The system maintains a more accurate global map by validating map information from multiple sources, preventing map corruption and enabling precise localization and navigation, enhancing productivity by continuous map updates without single-point failures.
Implementation Method 1
the AMR may use a light detection and ranging ('LiDAR') sensor to measure distances to obstacles from the AMR
Data Source
AI summary
Disclosed herein are devices, methods, and systems for map regeneration of an environment. The system includes a map manager configured to maintain a global map of the environment. The system also includes a robot configured to determine a correspondence between a mapped subarea of the environment and a global map of the environment and, based on the correspondence, transmit map data of the mapped subarea to a map manager. The map manager is configured to determine a quality metric of the map data of the mapped subarea with respect to the global map and update the global map with the map data of the mapped subarea based on the quality metric.