Robot Environment Map Updating for Stable Self-Localization
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Solution Overview
Problem
Conventional Simultaneous Localization and Mapping (SLAM) techniques used by robots result in map deterioration over time, leading to localization errors and the need for frequent manual remapping, which is costly and disruptive to robot operations.
Innovation Solution
A hybrid approach where robots use a static map for navigation, with periodic updates performed by a central computer appliance using batch-processed logged data from robots, allowing for high-quality map maintenance without interrupting robot operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If robots use online SLAM to continuously update maps during task execution, then the map can be updated in real-time, but the map deteriorates over time due to accumulated errors and drift in landmarks
Solution Approach 1:
The system performs preliminary mapping actions during idle periods or between task phases, rather than continuously during task execution. Robots collect sensor data and perform map updates when not actively performing tasks, preventing error accumulation while maintaining localization accuracy during critical task phases.
Solution Approach 2:
Map updates are performed periodically at designated intervals or transitions between task phases, rather than continuously. This periodic updating allows the system to maintain accurate maps without the continuous error accumulation that plagues real-time SLAM, by resetting the error accumulation cycle regularly.
2Measurement precision
If manual remapping is performed to correct significant map deviations, then localization accuracy can be restored, but robot operations must be taken offline causing time loss and reduced productivity
Solution Approach 1:
The system performs map corrections preliminarily during idle periods before they are needed for task execution. By completing map updates during non-task phases, the system ensures accurate maps are ready for task execution without interrupting productivity, as corrections are made in advance rather than reactively.
Solution Approach 2:
The system performs automatic map updates using its own sensor data and processing capabilities during idle periods, without requiring external manual intervention. This self-service approach maintains maps autonomously, eliminating the need for offline manual remapping and preserving continuous productivity.
3Reliability
If a static map is used for navigation, then localization is stable and accurate, but the map cannot adapt to environmental changes over time
Solution Approach 1:
The system implements a dynamic map updating strategy where the map transitions from static during task phases to updateable during idle phases. This dynamic approach allows the map to maintain stability during critical navigation periods while adapting to environmental changes during non-critical periods, resolving the contradiction between stability and adaptability.
Solution Approach 2:
The map alternates between static and updateable states periodically. During task phases, the map remains static for stable localization. During idle phases, the map becomes updateable to adapt to environmental changes. This periodic switching allows both stability and adaptability to coexist in different operational contexts.
Data Source
AI summary
A map updating technique offers the performance advantages gained by robots using a good-quality static map for autonomous navigation within an environment, while providing the convenience of automatic updating. In particular, one or more robots log data while performing autonomous navigation in the environment according to the static map, and a computer appliance, such as a centralized server, collects the logged data as historic logged data, and performs a map update process using the historic logged data. Such operations provide for periodic or as needed updating of the static map, based on observational data from the robot(s) that capture changes in the environment, without need for taking the robot(s) offline for the computation.