Moving Robot 3D Sensor Obstacle and Map Integration
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Solution Overview
Problem
Existing methods for moving robots require additional sensors and complex calculations for obstacle sensing, location estimation, and map creation, leading to increased computational burden and inefficiency.
Innovation Solution
The use of a 3D space recognition sensor allows for simultaneous obstacle sensing, location estimation, and map creation, eliminating the need for additional obstacle sensors and reducing computational complexity by integrating these processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional obstacle sensors and complex image processing algorithms are used for SLAM, then obstacle sensing precision and location estimation accuracy are improved, but device complexity and computational burden increase
Solution Approach 1:
The depth sensor serves multiple functions: it acts as both the SLAM sensing device for location estimation and map creation, and simultaneously functions as an obstacle sensing device. This multi-functionality eliminates the need for separate obstacle sensors while maintaining sensing precision, directly resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent merges the obstacle sensing function with the SLAM mapping function by using the same depth sensor for both purposes. Obstacles are identified as specific features within the generated map data, combining what were previously separate functions into a unified system, thereby reducing device complexity while preserving measurement precision
2Reliability
If additional obstacle sensors and independent obstacle management processes are implemented, then obstacle detection capability is improved, but the amount of calculation and processing time increase
Solution Approach 1:
The obstacle detection process is merged with the SLAM mapping process. Both functions process the same depth sensor data simultaneously, eliminating the need for separate obstacle management processes and reducing computational overhead, thereby improving reliability without increasing processing time
Solution Approach 2:
The SLAM processing system performs dual functions: creating the environmental map and simultaneously identifying obstacles within that map. This multi-functional approach allows the system to maintain reliable obstacle detection while avoiding the time penalty of separate processing pipelines
3Measurement precision
If separate maps are created for navigation and obstacle management, then path planning accuracy is improved, but device complexity and computational load increase
Solution Approach 1:
A single map generated by SLAM serves multiple purposes: it provides the environmental structure for navigation and simultaneously contains obstacle information for path planning. This unified map approach maintains path planning accuracy while eliminating the complexity of managing separate maps
Data Source
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AI summary
In accordance with one aspect of the present disclosure, a moving robot comprise a capturing unit configured to capture a three-dimensional (3D) image of surroundings of the moving robot, and extract depth image information of the captured 3D image, an obstacle sensor configured to sense obstacles using the 3D image captured by the capturing unit, a location estimator configured to estimate first location information of the moving robot within an area, excluding an obstacle area sensed by the obstacle sensor, using an inertia measurement unit and odometry and a controller configured to calculate second location information of the moving robot using the estimated first location information of the moving robot and the extracted depth image information, and create a map while excluding the obstacle area sensed by the obstacle sensor.