Robot Environment Map Updating for Drift-Free Self-Localization
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Solution Overview
Problem
Conventional SLAM-based map updating in robots leads to map deterioration due to drift, necessitating manual remapping, which is costly and disruptive, and existing methods lack efficient ways to update maps without taking robots offline.
Innovation Solution
A hybrid approach where robots operate with static maps that are periodically updated by a centralized computer appliance using logged data from multiple robots, performing batch-optimized SLAM to maintain high-quality maps without interrupting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If SLAM-based online map updating is used, then the map can be updated continuously during robot operation, but the map quality deteriorates over time due to drift errors
Solution Approach 1:
The system performs periodic offline map updates using accumulated logged data from multiple robots, rather than continuous online updates. This periodic batch processing allows errors to be corrected systematically while maintaining continuous operational capability during intervals between updates.
Solution Approach 2:
The system accumulates and logs sensor data and odometry information during normal robot operations before performing map updates. This preliminary data collection during the task phase enables comprehensive map refinement in subsequent offline processing without interrupting robot operations.
2Measurement precision
If manual remapping is performed to correct map deterioration, then map accuracy can be restored, but robot operations must be halted and significant time and cost are incurred
Solution Approach 1:
The system performs automatic map updates using logged data from robot operations, eliminating the need for manual remapping interventions. The offline map updating process runs autonomously using accumulated data, restoring map accuracy without requiring human operators to halt operations for remapping.
Solution Approach 2:
The system maintains continuous robot operations while accumulating data in the background, then performs map updates offline without interrupting operational continuity. This ensures useful actions (robot tasks) continue uninterrupted while map maintenance occurs in parallel.
3Reliability
If a static map is used for robot navigation, then localization is simple and reliable, but the map becomes outdated when environmental changes occur
Solution Approach 1:
The system transitions from a purely static map to a dynamically updated map through periodic offline processing. The map evolves over time by incorporating accumulated logged data from multiple robots, maintaining both the reliability of structured map data and the adaptability to environmental changes.
Solution Approach 2:
The system uses logged sensor data and odometry information from robot operations as feedback to continuously refine and update the map offline. This feedback mechanism ensures the map remains current with environmental changes while maintaining the structural reliability needed for accurate localization.
4Measurement precision
If batch-optimized SLAM is performed using logged data from multiple robots, then high-quality map updates can be generated, but processing time and computational resources increase
Solution Approach 1:
The system accumulates and logs data during normal robot operations before performing batch processing. This preliminary data collection during task execution enables comprehensive offline map updates without adding processing time to operational intervals, as processing occurs after data accumulation.
Solution Approach 2:
The system combines logged data from multiple robots into a single batch processing operation, generating improved map quality through aggregated information. This merging approach maximizes the value of accumulated data while performing updates offline, avoiding the need for continuous processing during operations.
Data Source
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AI summary
A map updating technique offers the performance advantages gained by robots (16) using a good-quality static map (12) for autonomous navigation within an environment (14), while providing the convenience of automatic updating. In particular, one or more robots (16) log data (22) while performing autonomous navigation in the environment (14) according to the static map (12), and a computer appliance (10), such as a centralized server, collects the logged data (22) as historic logged data (23), and performs a map update process using the historic logged data (23). Such operations provide for periodic or as needed updating of the static map (12), based on observational data from the robot(s) (16) that capture changes in the environment, without need for taking the robot(s) (16) offline for the computation.