Distance Sensor Projection Pattern Density Variation
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Solution Overview
Problem
High-density projection patterns in distance sensors lead to overlapping beam trajectories, complicating distance measurement, especially when objects are close, as the smaller distance results in higher pattern density, making it difficult to identify individual artifacts and affecting measurement accuracy.
Innovation Solution
The use of projection patterns with varying densities, where adjacent rows have different spatial densities of projection artifacts, allowing for easier identification of dots in lower density lines to accurately determine the trajectories and facilitate high-resolution distance measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-density projection patterns are used to achieve higher resolution distance measurements, then measurement precision is improved, but beam trajectories overlap making it difficult to identify individual artifacts
Solution Approach 1:
The projection pattern is segmented into multiple rows with different spatial densities. Some rows use high-density patterns for high-resolution measurements, while other rows use low-density patterns for easy artifact identification. This segmentation allows the system to overcome the overlap problem by providing reference rows with clearly distinguishable artifacts.
Solution Approach 2:
Different regions (rows) of the projection pattern are assigned different local qualities in terms of spatial density. High-density rows provide measurement precision where needed, while low-density rows provide identification reliability. This local variation in quality allows the system to simultaneously achieve both high resolution and reliable artifact identification.
2Measurement precision
If high-density patterns are projected to improve measurement resolution, then measurement precision is improved, but the complexity of analyzing the pattern increases due to overlapping trajectories
Solution Approach 1:
The pattern analysis is segmented into two distinct tasks: identifying artifacts in low-density rows (simpler) and measuring distances using high-density rows (more precise). This segmentation of the analysis process reduces overall complexity compared to attempting to identify and measure all artifacts in high-density patterns.
Solution Approach 2:
Low-density rows act as intermediary reference patterns that facilitate the analysis of high-density rows. By first identifying artifacts in the simpler low-density rows, the system establishes reference points that make the subsequent analysis of high-density rows more manageable and less complex.
3Measurement precision
If uniform high-density patterns are used across all rows, then measurement resolution is maximized, but the ease of identifying individual dots decreases
Solution Approach 1:
The projection pattern employs local quality variation by assigning different spatial densities to different rows. Low-density rows are specifically designed for ease of artifact identification, while high-density rows are optimized for measurement precision. This local differentiation resolves the contradiction between identification ease and measurement resolution.
Solution Approach 2:
The pattern is segmented into functional zones: identification rows with low density and measurement rows with high density. This functional segmentation allows each row type to excel at its specific task, with identification rows making dot detection easy and measurement rows providing high-resolution data.
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 enables accurate and high-resolution distance measurements by reducing overlap and improving the identification of individual dots, even at close distances, while maintaining the benefits of high-density patterns for higher resolution.
Implementation Method 1
projection systems (e.g., comprising lasers, diffractive optical elements, and/or other cooperating components) which project beams of light in a wavelength that is substantially invisible to the human eye (e.g., infrared)
Implementation Method 2
The beams of light spread out to create a pattern (of dots, dashes, or other artifacts) that can be detected by an appropriate light receiving system (e.g., lens, image capturing device, and/or other components)
Data Source
AI summary
In one example, a method includes instructing a pattern projector of a distance sensor to project a pattern of light onto the object, wherein the pattern comprise a plurality of parallel rows of projection artifacts, and wherein a spatial density of the projection artifacts in a first row of the plurality of parallel rows is different from a spatial density of the projection artifacts in a second row of the plurality of parallel rows, instructing a camera of the distance sensor to acquire an image of the object, where the image includes the pattern of light, and calculating a distance from the distance sensor to the object based on an analysis of the image.