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, require large amounts of memory, and often necessitate additional equipment, limiting their efficiency and cost-effectiveness, especially in environments with varying lighting conditions and transparent or reflective objects.
Innovation Solution
A method involving a robotic device that uses a camera to construct a map by taking depth measurements while moving in a boustrophedon pattern, combining overlapping fields of view to create a comprehensive map with minimal processing power and without the need for external beacons, using techniques such as depth comparison, overlap identification, and data transformation to build a 2D or 3D representation of the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Simultaneous Localization and Mapping (SLAM) methods are used to construct a map, then the map construction accuracy is improved, but the computational cost and memory requirements increase significantly
Solution Approach 1:
The patent segments the map construction process into distinct phases: a planning phase where a simplified graph representation is created, and an execution phase where the robot follows pre-planned paths. This segmentation allows the computationally intensive map planning to be done offline, while online operation only requires simple path following, thereby reducing real-time computational requirements while maintaining map accuracy.
Solution Approach 2:
The patent performs preliminary map construction and path planning before the robot begins its operational tasks. The complete map is built in advance using SLAM techniques, and navigation paths are pre-calculated. This preliminary action transfers the computational burden to an offline phase, allowing the robot to operate with minimal real-time processing while still benefiting from accurate mapping and navigation.
2Measurement precision
If SLAM methods with large complete state vectors and covariance matrices are used, then the pose estimation accuracy is improved, but the memory requirements and processing time increase
Solution Approach 1:
The patent extracts and separates the essential navigation information from the complete SLAM state vector. Instead of maintaining and processing the full state vector and covariance matrix online, the system extracts key features and pre-computes navigation paths offline. This extraction reduces the online memory requirements while preserving the accuracy benefits of SLAM for the specific task of navigation.
Solution Approach 2:
The patent performs preliminary computation of the complete state vector and covariance matrix offline during map construction. The results of these computations are stored and used to generate pre-planned paths. During online operation, the robot only needs to follow these pre-computed paths, eliminating the need to maintain large data structures in real-time memory while still achieving accurate pose estimation and navigation.
3Measurement precision
If additional equipment such as external IR pattern projection devices are used, then the mapping precision is improved, but the device complexity and cost increase
Solution Approach 1:
The patent enables the robot to perform mapping and navigation using only its onboard sensors and computational resources. The system processes sensor data from the robot's own perspective and constructs the map relative to the robot's frame of reference. This self-service approach eliminates the need for external infrastructure such as IR pattern projection devices, reducing system complexity and cost while maintaining mapping precision through sophisticated data processing algorithms.
Data Source
AI summary
Provided is a process executed by a robot, including: traversing, to a first position, a first distance in a backward direction; after traversing the first distance, rotating in a first rotation; after the first rotation, traversing, to a second position, a second distance in a third direction; after traversing the second distance, rotating 180 degrees in a second rotation such that the field of view of the sensor points in a fourth direction; after the second rotation, traversing, to a third position, a third distance in the fourth direction; after traversing the second distance, rotating 180 degrees in a third rotation such that the field of view of the sensor points in the third direction; and after the third rotation, traversing, to a fourth position, a fourth distance in the third direction.