3D Sensor Position Calibration with Multi-Point Noise Suppression
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Solution Overview
Problem
3D sensors provide spatial data that may not accurately reflect the actual spatial relationships, leading to incorrect calculations and decisions, especially when sensors with non-uniform or nonlinear spatial mapping are used, and traditional single-point calibration techniques fail to maintain accuracy at distances away from the calibration point.
Innovation Solution
A method involving multiple calibration positions distributed evenly throughout the workspace, recording position measurement signals, creating a noise suppression function based on signal deviations, and using these functions to de-noise sensor data for improved accuracy across a larger spatial area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-point calibration technique is used, then data accuracy is ensured near the calibration point, but data distortions increase with distance from the calibration point
Solution Approach 1:
The patent divides the calibration process into multiple discrete calibration positions distributed throughout the workspace. Instead of relying on a single calibration point, the system performs calibration at multiple locations (e.g., 8 or more positions) to create multiple local calibration references. This segmentation allows accurate position data to be maintained across the entire workspace by combining information from multiple calibration points through interpolation and transformation functions.
2Measurement precision
If multiple calibrations are used for multiple locations, then accuracy is improved at different positions, but switching between calibrations creates discontinuities
Solution Approach 1:
The patent merges multiple calibration results into a unified transformation model. By combining calibration data from multiple positions and integrating them through mathematical interpolation and transformation functions, the system creates a continuous and consistent calibration framework. This merging process eliminates discontinuities by ensuring smooth transitions between different calibration regions through the use of interpolation functions that maintain data consistency across the entire workspace.
3Adaptability or versatility
If a sensor with non-uniform or nonlinear spatial mapping is used, then the sensor can operate in complex spatial environments, but the spatial data does not accurately reflect actual spatial relationships
Solution Approach 1:
The patent applies parameter changes by transforming the sensor's non-uniform spatial mapping into a uniform coordinate system. Through calibration at multiple positions and mathematical transformations, the system adjusts and corrects the sensor's spatial parameters to compensate for non-uniformity. This allows the sensor to maintain its operational flexibility in complex environments while producing accurate spatial data that correctly represents actual spatial relationships through corrected coordinate transformations.
Data Source
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AI summary
A sensor should be able to better determine its position in a work area. For this purpose, a device and a method are provided in which position measurement signals are recorded at a plurality of calibration positions (c) of the sensor (1) in the three-dimensional space (2). A noise suppression function is created based on a deviation of the respective position measurement signal from predetermined position data at each calibration position of the sensor (1). Furthermore, a current position of the sensor (1) in the three-dimensional space (2) is recorded by the sensor (1). Finally, the recorded position is noise suppressed using multiple noise suppression functions from respective calibration positions (c).