Robot Image-to-Map Matching for Autonomous Location Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile robotic devices face challenges in efficiently navigating and servicing specific locations within environments without manual direction, as they lack effective image processing and mapping capabilities to autonomously identify and adapt to changing conditions.
Innovation Solution
The implementation of a robotic system that uses image processing to extract features and objects from captured images, matches them with a map of the environment, and instructs the robot to navigate to and service identified locations, incorporating features like pixel intensity analysis, clustering, and edge detection to determine locations and paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual direction is used to navigate the robot to specific locations, then the robot can reach the desired location, but the operation complexity and time consumption increase significantly
Solution Approach 1:
The robot autonomously identifies and navigates to service locations using image processing and map matching without requiring manual direction. The system processes images, extracts features, matches them with stored maps, and automatically determines navigation paths, enabling the robot to serve itself in location identification and navigation tasks.
Solution Approach 2:
The patent replaces manual mechanical navigation with an automated vision-based system. Instead of physically guiding the robot or using complex mechanical positioning systems, the invention uses image processing algorithms and computer vision to automatically identify locations and compute navigation paths, substituting mechanical/manual operations with intelligent software-based solutions.
2Adaptability or versatility
If the robot uses basic navigation without image processing, then the device complexity remains low, but the robot cannot accurately identify or adapt to changing environmental conditions
Solution Approach 1:
The system performs preliminary actions by capturing and processing images of the environment in advance, creating a visual map before navigation is needed. This pre-processing of environmental data enables the robot to quickly adapt to and identify locations during actual operation, as the heavy computational work of image analysis and map creation has already been completed.
Solution Approach 2:
The patent introduces an intermediary mapping system that bridges the gap between raw environmental images and navigation decisions. The created map serves as an intermediary representation that simplifies the complex task of real-time environmental analysis, allowing the robot to adapt to changing conditions by comparing current images against the stored map rather than processing raw sensor data from scratch each time.
3Productivity
If the robot autonomously identifies locations using image processing, then navigation efficiency improves, but the processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary image processing and map creation during periods when the robot is stationary or performing other tasks. By pre-processing images and creating environmental maps in advance, the computationally intensive work is completed beforehand, reducing the processing time required during actual navigation and location identification tasks.
Solution Approach 2:
The patent creates a simplified copy or representation of the environment in the form of a map that captures essential features and locations. This map copy allows the robot to quickly compare current sensor data against the stored representation, enabling fast location identification without re-processing all original image data, thus improving navigation efficiency while reducing real-time computational requirements.
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.