User-Assisted SLAM Mapping for Blocked Areas and Room Boundaries
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Solution Overview
Problem
Traditional SLAM approaches face challenges in creating accurate maps in heterogeneous environments, particularly due to sensor range limitations and the inability to detect blocked areas or determine room boundaries, leading to incomplete or warped maps.
Innovation Solution
User-assisted SLAM processes leverage human input through sensors on mobile devices and direct interaction to enhance sensor data, allowing robots to ask questions, receive corrections, and update maps, thereby improving map accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SLAM sensor data is used, then the robot can autonomously navigate, but the map accuracy and completeness deteriorate due to sensor range limitations and inability to detect blocked areas
Solution Approach 1:
The patent introduces human users as intermediaries who assist the robot in mapping. Users provide corrections, confirmations, and additional information about the environment that sensors cannot detect, such as blocked areas, room boundaries, and traversability. This intermediary human input compensates for sensor limitations without requiring complex additional sensor systems.
Solution Approach 2:
The system leverages the inherent knowledge and capabilities of human users who naturally understand their environment. Users self-serve by providing corrections and confirmations about map features, effectively using their own spatial awareness and environmental knowledge to improve map accuracy without requiring the robot to have advanced sensing capabilities.
2Loss of information
If traditional SLAM sensor data is used, then the robot can operate autonomously, but the map completeness deteriorates due to inability to identify traversable and non-traversable areas
Solution Approach 1:
The system implements feedback loops where users review and correct map features. Users provide feedback on traversable and non-traversable areas, room boundaries, and blocked regions. This feedback mechanism ensures complete environmental information is captured while maintaining reasonable ease of operation through intuitive interaction interfaces.
3Measurement precision
If user assistance is integrated into SLAM, then map accuracy improves through human input, but the automation level deteriorates due to requiring user interaction
Solution Approach 1:
The system applies partial automation where only critical map features requiring human judgment are subjected to user review and correction. Routine mapping operations proceed autonomously, while ambiguous or critical areas trigger user interaction. This partial action approach maintains high automation levels while achieving improved map accuracy through targeted user assistance.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for user assisted simultaneous localization and mapping (SLAM). These technologies can involve a robot including: one or more processors; one or more sensor subsystems; and one or more storage devices storing instructions that are operable, when executed by the one or more processors, to cause the agent to perform operations including: receiving first sensor data from the one or more sensor subsystems; determining the presence of a human based at least in part on the first sensor data; receiving second sensor data from the one or more sensor subsystems, the second sensor data indicating an action of the human; and determining at least one of mapping data and localization data based at least in part on the second sensor data.