Mobile Robot Localization Using Straight-Line Visual Map Matching
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Solution Overview
Problem
Localization of mobile robots in unstructured outdoor environments, such as cities and villages, is challenging due to the lack of precise publicly available maps and the dynamic nature of these environments, where existing solutions like GPS provide low precision, making it difficult for autonomous navigation.
Innovation Solution
A mobile robot equipped with at least two cameras that take visual images, a processing component to extract straight lines from these images, and compare them to stored map data using an iterative probabilistic algorithm, such as a particle filter, to determine its location and navigate autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS positioning system is used for outdoor localization, then the robot can obtain location information, but the precision is only 1-10 meters which is insufficient for autonomous navigation
Solution Approach 1:
The patent combines GPS positioning with visual recognition technology. The system integrates satellite-based location data with image processing algorithms that extract straight lines from camera images and match them against pre-stored map data, achieving centimeter-level precision (up to 10 cm error margin) by merging multiple localization approaches
Solution Approach 2:
The patent introduces straight line features extracted from visual images as an intermediary element. These extracted lines serve as mediators that link the robot's current position to the pre-stored map data, enabling precise localization by comparing geometric features rather than relying solely on GPS coordinates
2Measurement precision
If visual recognition with multiple cameras is used to achieve precise localization, then localization accuracy improves to 10 cm error, but the device complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the essential straight line features from visual images captured by multiple cameras. By focusing specifically on detecting and extracting straight lines rather than processing all image data, the system achieves precise localization while reducing computational complexity and processing requirements
Solution Approach 2:
The patent divides the localization task into separate processing streams: one for capturing images with multiple cameras, another for extracting straight line features from images, and a third for matching these features with pre-stored map data. This segmentation allows each component to be optimized independently
3Speed
If the robot uses pre-stored map data for localization, then localization speed improves, but the map data becomes outdated quickly in dynamic environments
Solution Approach 1:
The patent implements a feedback mechanism where the robot continuously compares extracted straight line features from current images with those in the pre-stored map data. When discrepancies are detected indicating environmental changes, the system updates the map data to reflect the current state, maintaining both speed and adaptability
Data Source
AI summary
A mobile delivery robot has at least one memory component containing at least map data; at least two cameras adapted to take visual images; and at least one processing component. The at least one processing component is adapted to at least extract straight lines from the visual images taken by the at least two cameras and compare them to the map data to at least localize the robot. The mobile robot employs a localization method which involves taking visual images with at least two cameras; extracting straight lines from the individual visual images with at least one processing component; comparing the extracted features with existing map data; and outputting a location hypothesis based on said comparison.