Robot Semantic Localization Using Object-Based Zone Recognition
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Solution Overview
Problem
Autonomous robots face challenges in accurate localization and mapping due to inaccuracy in determining their physical location, especially in unknown environments or when their localization system fails, which affects their ability to perform accurate path planning and navigation.
Innovation Solution
The system employs place and object recognition in conjunction with a mapping hierarchy to improve localization accuracy and efficiency by identifying semantic zones and their characteristics, allowing the robot to determine its location within a property using sensor data and a structured mapping hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional localization methods (proximity beacons, NFC, WiFi, Bluetooth) are used to determine robot location, then the robot can obtain location information, but the accuracy is insufficient especially in unknown environments or when localization system fails
Solution Approach 1:
The patent introduces semantic zone information as an intermediary layer between traditional localization methods and the robot's navigation system. By detecting objects and determining semantic zones (e.g., kitchen, bedroom, bathroom) from sensor data, the system creates a meaningful interpretation layer that enhances location accuracy without relying solely on traditional beacon-based methods. This intermediary semantic layer allows the robot to localize itself even when traditional localization systems fail or in unknown environments.
2Measurement precision
If the robot uses sensor data and object recognition to determine location, then localization accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the environment into discrete semantic zones based on object detection and spatial relationships. Instead of processing all sensor data to determine location, the system divides the property into meaningful segments (semantic zones) with specific characteristics. This segmentation allows the robot to identify its location by matching detected objects to predefined zone templates, significantly reducing computational complexity while maintaining high localization accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-defining semantic zones and their characteristic objects during the mapping phase. Before the robot needs to localize itself, the environment is already segmented into meaningful zones with identified key objects. When localization is needed, the robot simply compares its current sensor readings against these pre-established zone profiles, avoiding the need for complex real-time analysis and reducing processing time.
3Productivity
If the robot performs simultaneous localization and mapping (SLAM), then it can build property mappings while tracking its location, but errors in location determination propagate to mapping inaccuracies
Solution Approach 1:
The patent implements feedback by using detected semantic zones and object locations to continuously verify and correct the robot's estimated position during SLAM operations. As the robot moves through the environment and detects objects, it compares observed object positions against its current map and location estimate. Discrepancies provide feedback that allows the system to correct localization errors before they propagate to mapping inaccuracies, thereby improving both localization precision and overall mapping accuracy while maintaining productive simultaneous operation.
Data Source
AI summary
Methods, systems, and apparatus for receiving data that represents a portion of a property that was obtained by a robot, identifying, based at least on the data, objects that the data indicates as being located within the portion of the property, determining, based on the objects, a semantic zone type corresponding to the portion of the property, accessing a mapping hierarchy for the property, wherein the mapping hierarchy for the property specifies semantic zones of the property that have corresponding semantic zone types and are associated with locations at the property, and specifies characteristics of the semantic zones, and selecting, from among the semantic zones and based at least on the semantic zone type and the data, a particular semantic zone, and setting, as a current location of the robot at the property, a particular location at the property associated with the particular semantic zone.