Method for constructing a map while performing work
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Solution Overview
Problem
Current mapping techniques for autonomous robotic devices are computationally expensive, requiring substantial processing power and memory, and often necessitate additional equipment, which can limit their speed and performance, especially in environments with similar features and measurement noise.
Innovation Solution
A method for mapping a workspace using a robotic device that captures data on its position and movement, recognizes areas, and generates a map by combining depth measurements from overlapping fields of view, allowing the device to move efficiently and accurately construct a map without the need for external beacons or extensive processing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SLAM methods using large amounts of captured points and features are used, then map construction accuracy is improved, but computational cost and processing time increase substantially
Solution Approach 1:
The patent extracts only the essential features needed for mapping by using a segmented laser scanner that captures depth information in specific angular segments. Instead of processing all captured points, the system selectively processes only the relevant depth measurements from overlapping fields of view, thereby reducing computational cost while maintaining map construction accuracy.
Solution Approach 2:
The laser scanner is divided into multiple segments that capture depth information from different angular fields of view. This segmentation allows the system to process smaller, manageable portions of data independently and combine them, reducing the overall computational burden while preserving the accuracy needed for complete environment mapping.
2Measurement precision
If probabilistic processing and particle filtering are used to estimate robot pose and feature positions, then positioning accuracy is improved, but memory requirements and processing complexity increase
Solution Approach 1:
The patent uses inexpensive depth measurements from segmented laser scans as temporary data that are processed and discarded after contributing to the map. Instead of maintaining complex probabilistic models and particle filters, the system uses simple depth data that can be quickly processed and discarded, reducing both memory requirements and processing complexity while still achieving accurate positioning through the segmentation and overlap matching approach.
3Measurement precision
If additional equipment such as external IR pattern projectors are used, then mapping capability is improved, but device complexity and cost increase
Solution Approach 1:
The robotic device uses its own onboard segmented laser scanner to perform mapping without requiring external IR pattern projectors or other additional equipment. The system services itself by capturing depth information from multiple overlapping fields of view using its integrated sensor, eliminating the need for external mapping equipment and reducing overall device complexity.
4Measurement precision
If high rate data collection using Laser Distance Sensors is used, then mapping detail is improved, but sensitivity to lighting and transparent/reflective objects worsens
Solution Approach 1:
The laser scanner is segmented into multiple angular sections that capture depth information from different perspectives. This segmentation allows the system to overcome sensitivity issues with transparent and reflective objects by capturing data from multiple angles, where objects that are difficult to detect from one angle may be clearly visible from another, thereby improving reliability while maintaining mapping detail.
Data Source
AI summary
Provided is a method including: capturing, with at least one sensor of a robot, first data indicative of the position of the robot in relation to objects within the workspace and second data indicative of movement of the robot; recognizing, with a processor of the robot, a first area of the workspace based on observing at least one of: a first part of the first data and a first part of the second data; generating, with the processor of the robot, at least part of a map of the workspace based on at least one of: the first part of the first data and the first part of the second data; generating, with the processor of the robot, a first movement path covering at least part of the first recognized area; actuating, with the processor of the robot, the robot to move along the first movement path.