Robotic Arm and Depth Sensor Registration Using Sparse Calibration
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Solution Overview
Problem
Precise registration between depth sensors and robotic arms is challenging due to various error sources, including measurement inaccuracies, mechanical movements, and non-linear biases, leading to inaccuracies in manipulation systems.
Innovation Solution
A non-parametric technique for automatic in-situ calibration and registration of depth sensors and robotic arms using sparse sampling, point cloud formation, and interpolation, which incorporates intrinsic sensor parameters without requiring an explicit model of sensor biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a linear transformation matrix is used to map coordinates between depth sensor and robotic arm, then the mapping process is simple and fast, but measurement precision and manufacturing precision deteriorate due to inability to compensate for non-linear biases
Solution Approach 1:
The patent changes the transformation model from a simple linear matrix to a non-linear model using sparse sampling and interpolation. By collecting calibration data at multiple discrete points and using interpolation algorithms, the system adapts the transformation parameters to compensate for non-linear sensor biases while maintaining computational efficiency.
Solution Approach 2:
The patent performs preliminary calibration by collecting sensor and arm coordinate data at multiple predefined workspace positions before actual operation. This pre-collected calibration data is stored and used during runtime for accurate coordinate transformation, eliminating the need for real-time complex calculations.
2Manufacturing precision
If dense sampling of workspace is performed during calibration, then manufacturing precision and measurement precision improve, but loss of time increases due to extensive calibration points
Solution Approach 1:
The patent applies sparse sampling, collecting calibration data at a limited number of strategically chosen workspace positions rather than densely sampling the entire workspace. This partial sampling approach, combined with interpolation, achieves sufficient registration accuracy while dramatically reducing calibration time and computational burden.
Solution Approach 2:
The patent creates a virtual point cloud model of the workspace based on sparse calibration measurements. This digital model is then used for interpolation to estimate coordinates at unmeasured positions, effectively copying the transformation behavior from sampled points to the entire workspace without physically measuring every point.
3Measurement precision
If explicit model of sensor intrinsics or biases is utilized, then measurement precision improves through compensation, but device complexity increases
Solution Approach 1:
The patent extracts and compensates for sensor biases implicitly through the non-linear transformation process rather than explicitly modeling each bias source. By separating the bias compensation function from the main transformation and handling it through interpolation of calibration data, the system achieves accurate coordinate mapping without requiring complex explicit models of sensor intrinsics.
Data Source
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AI summary
Various technologies described herein pertain to automatic in-situ calibration and registration of a depth sensor and a robotic arm, where the depth sensor and the robotic arm operate in a workspace. The robotic arm can include an end effector. A non-parametric technique for registration between the depth sensor and the robotic arm can be implemented. The registration technique can utilize a sparse sampling of the workspace (e.g., collected during calibration or recalibration). A point cloud can be formed over calibration points and interpolation can be performed within the point cloud to map coordinates in a sensor coordinate frame to coordinates in an arm coordinate frame. Such technique can automatically incorporate intrinsic sensor parameters into transformations between the depth sensor and the robotic arm. Accordingly, an explicit model of intrinsics or biases of the depth sensor need not be utilized.