Mobile Robot Semantic Map Transfer With Conflict Validation
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Solution Overview
Problem
Autonomous mobile robots face challenges in maintaining accurate and consistent semantic maps over time, as semantic annotations can become invalid due to changes in the environment or transfer errors, leading to incorrect navigation and mission execution.
Innovation Solution
A system and method for generating and managing semantic maps in mobile robots, which includes a controller circuit that senses occupancy information, transfers semantic annotations between missions, detects and resolves conflicts, and updates the map based on user feedback and environmental changes, ensuring semantic consistency and validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If semantic annotations are transferred between missions to maintain map consistency, then the robot can reuse valid semantic information, but transfer errors and invalid annotations may accumulate and corrupt the map
Solution Approach 1:
The system implements feedback mechanisms where semantic annotations are continuously validated against current sensor data and occupancy information. Invalid annotations are detected through consistency checks that compare transferred semantic data with real-time environmental sensing, allowing the robot to identify and correct erroneous annotations while preserving valid ones.
Solution Approach 2:
Before transferring semantic annotations between missions, the system performs preliminary validation checks to ensure annotation correctness. This includes verifying spatial consistency, object permanence, and contextual validity before incorporating transferred annotations into the current semantic map, preventing corrupt data from being integrated.
2Reliability
If the semantic map is updated frequently to reflect environmental changes, then the map remains current and accurate, but computational resources and processing time are consumed
Solution Approach 1:
The system employs periodic update strategies where semantic map validation and updates occur at scheduled intervals or triggered by specific events rather than continuously. This includes updating the semantic map after completing full cleaning cycles, upon detecting significant environmental changes, or at predetermined time intervals, balancing map currency with energy conservation.
Solution Approach 2:
The update frequency of the semantic map is made dynamic rather than static. The system adjusts update intervals based on environmental stability, robot activity patterns, and detected change rates. When the environment is stable, updates are reduced; when changes are detected, updates are triggered, optimizing the balance between accuracy and energy consumption.
3Stability of the object's composition
If strict validation rules are applied to semantic annotations, then map consistency is maintained, but legitimate semantic information may be rejected due to minor inconsistencies
Solution Approach 1:
The system implements adjustable validation parameters and tolerance thresholds that can be modified based on environmental context and confidence levels. Rather than applying fixed strict rules, the validation criteria adapt to allow legitimate variations in semantic annotations while still filtering out invalid data, using parameter adjustments to balance consistency with information retention.
4Measurement precision
If the robot performs comprehensive environmental sensing to ensure accurate semantic mapping, then mapping precision is improved, but the time and energy required for each mission increases
Solution Approach 1:
The system applies partial sensing strategies where comprehensive environmental scanning is performed only in areas where semantic accuracy is critical or where changes are detected. Rather than sensing the entire environment uniformly, the robot focuses sensing resources on regions requiring validation, allowing acceptable precision in stable areas while maintaining high accuracy in dynamic or important zones.
Data Source
AI summary
Described herein are systems, devices, and methods for maintaining a valid semantic map of an environment for a mobile robot. A mobile robot comprises a drive system, a sensor circuit to sense occupancy information, a memory, a controller circuit, and a communication system. The controller circuit can generate a first semantic map corresponding to a first robot mission using first occupancy information and first semantic annotations, transfer the first semantic annotations to a second semantic map corresponding to a subsequent second robot mission. The control circuit can generate the second semantic map that includes second semantic annotations generated based on the transferred first semantic annotations. User feedback on the first or the second semantic map can be received via a communication system. The control circuit can update first semantic map and use it to navigate the mobile robot in a future mission.