Mobile Robot Visual Mapping With Straight-Line Localization
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Solution Overview
Problem
Existing mapping technologies for mobile robots in unstructured outdoor environments face challenges due to the lack of precise publicly available maps, rapid changes in environments, and the limitations of GPS systems, which provide insufficient precision for autonomous navigation.
Innovation Solution
The use of multiple cameras on a mobile robot to take visual images, extract straight lines, and generate map data, combined with data from other sensors like GPS and Lidar, to build and refine maps simultaneously with localization, employing iterative algorithms to associate extracted features with physical objects and landmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS systems are used for localization, then the robot can operate in unstructured outdoor environments, but the precision is insufficient for autonomous navigation
Solution Approach 1:
The patent combines GPS data with visual features (straight lines extracted from images) and other sensor data (Lidar, odometry) to create a multi-source localization system. This merging allows the robot to achieve high precision localization in unstructured outdoor environments by compensating for GPS limitations through visual landmark recognition and map matching.
Solution Approach 2:
The patent introduces map data containing straight line features as an intermediary between GPS and the robot's position estimation. The GPS provides coarse location, which is then refined by matching visual features against pre-built maps, acting as an intermediary layer that transforms low-precision GPS data into high-precision localization.
2Measurement precision
If multiple cameras are used to extract straight lines and generate map data, then mapping precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of map generation into distinct processing stages: image capture by multiple cameras, straight line extraction from images, feature association with physical objects, and map data construction. This segmentation allows each component to be optimized independently and processed in a systematic pipeline, managing complexity through structured decomposition.
Solution Approach 2:
The system uses the robot's own captured images to automatically extract straight line features and generate map data without requiring manual surveying or pre-existing detailed maps. The robot performs self-localization and self-mapping by processing its own sensory data through iterative algorithms, reducing external dependencies and operational complexity.
3Measurement precision
If iterative algorithms are used to associate extracted features with physical objects, then localization accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-building maps containing straight line features and physical object associations before the robot needs to localize. During operation, the robot only needs to extract features from current images and match them against the pre-processed map data, significantly reducing real-time processing requirements while maintaining high localization accuracy.
Solution Approach 2:
The iterative algorithm continuously refines localization estimates by repeatedly associating extracted straight line features with map features. This continuous processing allows the system to converge on accurate positions while maintaining real-time performance through optimized iteration loops that balance precision requirements with processing speed.
Data Source
AI summary
A mobile robot is configured to navigate on a sidewalk and deliver a delivery to a predetermined location. The robot has a body and an enclosed space within the body for storing the delivery during transit. At least two cameras are mounted on the robot body and are adapted to take visual images of an operating area. A processing component is adapted to extract straight lines from the visual images taken by the cameras and generate map data based at least partially on the images. A communication component is adapted to send and receive image and/or map data. A mapping system includes at least two such mobile robots, with the communication component of each robot adapted to send and receive image data and/or map data to the other robot. A method involves operating such a mobile robot in an area of interest in which deliveries are to be made.