Sidewalk Delivery Robot Mapping Using Straight-Line Visual Features
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Solution Overview
Problem
Current mapping technologies for mobile robots in unstructured outdoor environments face challenges due to the lack of precise publicly available maps and the dynamic nature of these environments, where existing solutions often rely on GPS systems with limited precision and are not economically practical for wide-range applications.
Innovation Solution
A method using multiple cameras to take visual images, extract straight lines, and generate map data, combining these images and lines into a single reference frame, and utilizing iterative algorithms to associate extracted features with physical objects, while also incorporating data from other sensors like GPS and Lidar to refine the map accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GPS systems are used for localization in unstructured outdoor environments, then the robot can navigate without detailed pre-existing maps, but the precision is limited to 1-10 meters which is insufficient for autonomous robotic navigation
Solution Approach 1:
The patent combines GPS localization with visual odometry and feature-based mapping systems. The GPS provides coarse location estimates while computer vision algorithms process camera images to extract visual features and calculate precise relative position changes. This merged system achieves both broad adaptability to unstructured environments and high precision localization by compensating for GPS limitations through visual feedback.
Solution Approach 2:
The patent introduces visual features extracted from camera images as an intermediary between GPS coordinates and robot pose estimation. Instead of relying directly on low-precision GPS data, the system uses visual landmarks and feature points as intermediate references to calculate accurate relative movements and positions, thereby achieving high-precision localization in GPS-denied or low-precision environments.
2Measurement precision
If multiple cameras are used to take visual images for mapping, then the map accuracy and precision are improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent divides the visual mapping task across multiple cameras, each capturing images from different viewpoints. The system segments the environment into multiple visual fields of view and processes images from each camera independently through feature extraction and matching algorithms. This segmentation approach enables comprehensive environmental mapping with high precision while distributing computational load across parallel processing streams for each camera.
Solution Approach 2:
The patent employs a unified computer vision processing pipeline that handles images from multiple cameras using the same feature extraction, matching, and pose estimation algorithms. The multi-camera system is designed with universal mounting configurations and synchronized timing that allow all cameras to function within a single integrated mapping framework, reducing overall system complexity despite the increased number of sensors.
3Ease of manufacture
If visual techniques are used for SLAM, then the system is economically practical and can operate in dynamic environments, but the precision and reliability are reduced compared to laser rangefinders
Solution Approach 1:
The patent changes the operational parameters of visual SLAM by using multi-camera stereo vision geometry and temporal correlation of visual features across multiple frames. Instead of relying on single-frame monocular cues, the system exploits depth information from multiple viewpoints and tracks feature movements over time, significantly improving localization reliability and robustness while maintaining the economic advantages of using standard camera sensors rather than expensive laser rangefinders.
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.