3D Workpiece Pose Registration Using Laser Point Cloud Tessellation
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Solution Overview
Problem
Existing methods for 3D pose registration in robotic devices, such as camera-based and laser tracking systems, are often expensive, time-consuming, and lack sufficient accuracy, particularly in manufacturing environments, where precise calibration and high accuracy are required.
Innovation Solution
A registration system using mounted lasers and sensors to project and detect laser returns, converting them into a 3D point cloud, filtering visible cells to form a tessellation included set, and solving for the 3D pose of a workpiece relative to a robotic device with high accuracy using optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera-based methods with special physical features and precise lens calibration are used, then pose registration can be achieved, but the system becomes complex and costly, and accuracy greater than one millimeter cannot be guaranteed
Solution Approach 1:
The patent extracts and removes the requirement for special physical features (fiducials) and precise lens calibration from the system. By using a featureless calibration object and eliminating calibration steps, the solution simplifies the system while maintaining high pose registration accuracy through direct laser scanning and point cloud registration techniques
Solution Approach 2:
The patent replaces the optical-mechanical calibration system (cameras requiring lens calibration) with a direct laser ranging system. The laser scanner directly measures distances to create point clouds, eliminating the need for complex optical calibration while achieving superior accuracy through time-of-flight or phase-shift measurements
2Measurement precision
If laser tracking methods with highly reflective features and physically actuated lasers are used, then pose registration accuracy improves, but the system becomes very expensive and time-consuming (approximately four hours to calibrate)
Solution Approach 1:
The patent performs preliminary scanning to generate a complete point cloud of the calibration object before any pose registration calculations. This pre-acquired point cloud serves as a reference database that enables rapid pose determination without time-consuming calibration procedures during actual measurement operations
Solution Approach 2:
The patent creates a digital copy (point cloud) of the calibration object's geometry that can be repeatedly used for pose registration without requiring physical re-calibration. The point cloud serves as a virtual reference model that enables rapid comparison and alignment with new laser scans, eliminating repeated calibration time
3Measurement precision
If existing laser tracking methods are used, then pose registration accuracy is improved, but the system becomes very expensive
Solution Approach 1:
The patent uses a simple, inexpensive calibration object with a known geometry (such as a sphere or other basic shape) that can be manufactured at low cost. This disposable-like calibration object replaces expensive, sophisticated calibration artifacts, achieving high accuracy through its simple geometric features while being far more cost-effective
Solution Approach 2:
The patent creates a multi-functional system where the laser scanner serves both as a measurement device and a calibration device. The same laser scanner used for final pose registration is also used to create the reference point cloud, eliminating the need for separate calibration equipment and reducing overall system cost while maintaining accuracy
4Productivity
If discrete point sampling and ICP algorithms are used, then pose registration can be performed, but accuracy decreases and CPU overhead increases
Solution Approach 1:
The patent uses the complete, dense point cloud data from laser scanning rather than sampling only discrete points. By utilizing all available measurement points (excessive action) rather than a subset, the system achieves superior accuracy while the efficient registration algorithm maintains acceptable processing speeds by optimizing the matching computation
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 cost-effective and efficient method for achieving high-accuracy 3D pose registration, facilitating precise manufacturing tasks by eliminating the need for internal feature alignment and reducing CPU overhead.
Implementation Method 1
one or more mounted lasers of the robotic device and reflected by the workpiece
Implementation Method 2
laser returns from laser rays projected from the one or more mounted lasers and reflected by the workpiece
Data Source
AI summary
In an example, a system for registering a three-dimensional (3D) pose of a workpiece relative to a robotic device is disclosed. The system comprises the robotic device, where the robotic device comprises one or more mounted lasers. The system also comprises one or more sensors configured to detect laser returns from laser rays projected from the one or more mounted lasers and reflected by the workpiece. The system also comprises a processor configured to receive a tessellation of the workpiece, wherein the tessellation comprises a 3D representation of the workpiece made up of cells, convert the laser returns into a 3D point cloud in a robot frame, based on the 3D point cloud, filter visible cells of the tessellation of the workpiece to form a tessellation included set, and solve for the 3D pose of the workpiece relative to the robotic device based on the tessellation included set.