Multi-Camera Robot Mapping Using Straight Lines for Outdoor Localization
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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 utilizing multiple cameras to take visual images, extract straight lines, and generate map data, combining these images and extracted features into a single reference frame, and using iterative algorithms to associate lines 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
1Measurement precision
If GPS systems are used for localization in unstructured outdoor environments, then the system can operate in wide areas, but the precision is limited to 1-10 meters which is insufficient for autonomous navigation
Solution Approach 1:
The patent combines multiple sensors (cameras, GPS, Lidar, odometers, gyroscopes, accelerometers, magnetometers, time of flight cameras, and radar sensors) into an integrated sensing system. This merging allows the robot to achieve high-precision localization (better than GPS alone) while maintaining the ability to operate in wide unstructured outdoor environments, resolving the contradiction between precision and operational scope.
Solution Approach 2:
The patent creates a multi-functional localization system that can operate in both GPS-denied environments (using visual features, inertial sensors, and Lidar) and GPS-available environments (combining all sensor inputs). This universal system adapts to different conditions, providing high precision regardless of whether GPS is available, thus resolving the precision limitation while maintaining system versatility.
2Ease of manufacture
If visual techniques with multiple cameras are used for mapping, then the system is economically practical and can operate in dynamic environments, but the device complexity increases
Solution Approach 1:
The patent employs multiple cameras that serve dual purposes: they capture visual images for mapping and simultaneously provide visual odometry data for localization. This multi-functionality allows the system to be economically practical (using standard camera technology) while achieving both mapping and localization goals, offsetting the increased device complexity through efficient resource utilization.
Solution Approach 2:
The visual features extracted from camera images serve multiple functions within the system. The same visual features are used for both mapping the environment and for localizing the robot's position within that map. This self-service approach reduces the need for separate specialized sensors, making the system more economically practical despite the multi-camera configuration.
3Extent of automation
If SLAM is performed simultaneously with localization, then the robot can explore unknown surroundings autonomously, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the SLAM process into distinct but coordinated components: visual feature extraction from camera images, visual odometry calculation for motion estimation, map building using extracted features, and localization within the constructed map. This segmentation allows each component to be optimized independently and processed efficiently, reducing the overall computational complexity while maintaining autonomous exploration capability.
Solution Approach 2:
The system performs preliminary action by pre-extracting visual features from camera images and pre-building map structures before full SLAM operation is required. This preliminary processing reduces the real-time computational burden during autonomous exploration, allowing the robot to maintain high automation levels without overwhelming processing requirements.
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.