Robotic Workspace Perimeter Mapping Using Translated Depth Data
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Solution Overview
Problem
Current robotic mapping techniques require significant computational power and often rely on inaccurate sensor statistics, leading to poor performance, and may necessitate additional components like beacons, which increase costs and complexity, especially in consumer environments.
Innovation Solution
A method using one or more processors to obtain and translate depth data from imaging devices, such as cameras or LIDAR, to create a spatial map of a workspace, adjusting influence based on sensor impairment and utilizing odometry data when exteroceptive sensors fail, to efficiently mark the perimeter of a workspace with reduced computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EKF technique is used to map the environment with feature points, then mapping accuracy is improved, but computational power requirement increases significantly
Solution Approach 1:
The patent extracts only the essential perimeter information from the environment rather than processing all feature points. By focusing specifically on perimeter detection and using a simplified coordinate system approach, the system achieves adequate mapping accuracy without the computational burden of EKF techniques that process comprehensive feature data.
Solution Approach 2:
The patent changes the parameter representation from complex EKF state vectors and covariance matrices to simple coordinate systems with perimeter points. This parameter transformation reduces the computational complexity while maintaining the essential functionality of environment mapping and perimeter identification.
2Area of stationary object
If distance sensor rotates 360-degrees to map the environment, then complete environment coverage is achieved, but operational time is lost
Solution Approach 1:
The patent performs preliminary perimeter detection during the robot's normal movement and translation tasks. Instead of dedicating separate time for 360-degree sensor rotation, the system collects perimeter data incrementally as the robot moves through the environment, allowing mapping to occur concurrently with operational tasks.
Solution Approach 2:
The patent maintains continuous useful action by allowing the robot to perform work tasks simultaneously with perimeter mapping. The depth sensor continuously collects data during normal operation without requiring pause for dedicated mapping cycles, ensuring both productivity and mapping functionality operate continuously.
3Measurement precision
If additional components like beacons are added for mapping, then positioning accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent enables the robotic device to perform its own positioning and mapping using its existing depth sensor and movement capabilities. The system uses its own sensor data and odometry information to determine its position relative to the perimeter, eliminating the need for external beacons or additional positioning components.
Solution Approach 2:
The patent makes the depth sensor serve multiple functions: it is used both for primary work tasks (such as cleaning or inspection) and for perimeter detection and mapping. This multi-functionality eliminates the need for separate dedicated mapping components like beacons, reducing overall system complexity while maintaining positioning accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a computationally inexpensive and cost-effective solution for marking the perimeter of a workspace, allowing robotic devices to operate autonomously with minimal external control, while reducing the need for additional components and improving mapping accuracy.
Implementation Method 1
the first depth data indicates a first distance from a robot at a first position to a surface of, or in, a workspace
Implementation Method 2
depth data from imaging devices, such as cameras or LIDAR
Data Source
AI summary
Provided is a process, including: obtaining, with one or more processors, first depth data, wherein: the first depth data indicates a first distance from a robot at a first position to a surface of, or in, a workspace in which the robot is disposed, the first depth data indicates a first direction in which the first distance is measured, the first depth data indicates the first distance and the first direction in a frame of reference of the robot, and the frame of reference of the robot is different from a frame of reference of the workspace; translating, with one or more processors, the first depth data into translated first depth data that is in the frame of reference of the workspace; and storing, with one or more processors, the translated first depth data in memory.