Autonomous Robot Image-to-Map Matching for Location Identification
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Solution Overview
Problem
Mobile robotic devices face challenges in efficiently navigating and servicing specific locations within an environment without manual direction, as they lack effective methods to identify and match visual cues from images with their internal maps.
Innovation Solution
The solution involves a robotic system that receives images from an external imaging device, processes pixel intensity values to extract objects and features, matches these with a map of the environment, and instructs the robot to navigate to identified locations, using techniques such as DBSCAN clustering, entropy analysis, and convolution methods to determine the best match.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a mobile robotic device uses manual direction or remote control to navigate to specific locations, then the device can reach the desired location, but the operation complexity and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by creating a detailed map of the environment with labeled locations and features before navigation is needed. When a destination is specified, the robot can quickly match it against the pre-created map rather than exploring or manually navigating, thus reducing time loss while maintaining ease of operation
Solution Approach 2:
The system creates a digital copy of the physical environment in the form of a structured map data structure that mirrors the real-world layout, locations, and features. This digital twin enables rapid information retrieval and navigation decision-making without physical exploration, resolving the contradiction between autonomous operation and time efficiency
2Measurement precision
If the robotic device processes detailed image data to accurately identify locations, then navigation precision improves, but computational complexity and processing time increase
Solution Approach 1:
The image processing system is segmented into distinct functional modules: feature detection extracts key visual elements, object recognition identifies specific items, and map matching correlates findings with the digital map. This segmentation allows each module to specialize in one task, improving overall accuracy while managing complexity through modular design
Solution Approach 2:
The patent introduces an intermediary data structure that bridges raw image data and the navigation system. This intermediate representation captures essential spatial and semantic information from images in a format suitable for map comparison, enabling accurate location identification without requiring the full complexity of raw image processing to be handled by the navigation system
Data Source
AI summary
Provided are operations including: receiving, with one or more processors of a robot, an image of an environment from an imaging device separate from the robot; obtaining, with the one or more processors, raw pixel intensity values of the image; extracting, with the one or more processors, objects and features in the image by grouping pixels with similar raw pixel intensity values, and by identifying areas in the image with greatest change in raw pixel intensity values; determining, with the one or more processors, an area within a map of the environment corresponding with the image by comparing the objects and features of the image with objects and features of the map; and, inferring, with the one or more processors, one or more locations captured in the image based on the location of the area of the map corresponding with the image.