3D Scanner Depth Sensor Mapping Stability Feedback
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Solution Overview
Problem
Existing three-dimensional scanning technologies face challenges in creating stable and accurate three-dimensional maps of complex spaces, such as production sites and elevator shafts, due to the need for trial-and-error determination of optimal camera viewpoints and lack of evaluation for self-position estimation, leading to potential errors and delays in measurement processes.
Innovation Solution
A three-dimensional scanner equipped with a depth sensor and a scanner main body that extracts features from measurement data, calculates the sensor's position and direction, determines optimal movement candidates, evaluates mapping stability, and presents moving directions or speeds to users based on evaluation results, ensuring smoother and more accurate map creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual scanning method is used to create three-dimensional map, then flexibility in measurement is improved, but mapping accuracy and stability deteriorate due to trial-and-error determination of optimal viewpoints
Solution Approach 1:
The system evaluates mapping stability for each candidate viewpoint using extracted features, and uses this evaluation feedback to guide the selection of optimal viewpoints and sensor movement paths, replacing trial-and-error with systematic feedback-driven decision making
Solution Approach 2:
The system performs preliminary feature extraction and mapping stability evaluation for multiple candidate viewpoints before actual scanning, allowing pre-determination of optimal viewpoints and movement paths that ensure high mapping accuracy from the start
2Adaptability or versatility
If trial-and-error method is used to determine optimal camera viewpoints, then adaptability to different spaces is improved, but measurement time and productivity deteriorate
Solution Approach 1:
The system performs preliminary feature extraction and mapping stability evaluation for multiple candidate viewpoints before actual scanning, allowing pre-determination of optimal viewpoints and movement paths that ensure high mapping accuracy from the start
Solution Approach 2:
The system automatically evaluates mapping stability and determines optimal viewpoints and sensor movement paths without requiring user trial-and-error, making the system self-guiding and adaptive to any three-dimensional space
3Device complexity
If self-position estimation is not evaluated, then device complexity is reduced, but mapping stability and reliability deteriorate
Solution Approach 1:
The system introduces mapping stability evaluation as a feedback mechanism that assesses the quality of self-position estimation and feature matching, providing guidance for improving mapping reliability without requiring complex additional hardware
Data Source
AI summary
In a three-dimensional scanner, a scanner main body calculates the position and direction of a depth sensor. The scanner main body also determines a movement candidate, which is a candidate for a position and direction to/in which the depth sensor is to be moved next. Then, the scanner main body acquires a feature within the movement candidate, which is the feature observable by the depth sensor from the movement candidate, and evaluates the stability of mapping performed from the movement candidate through use of the feature within the movement candidate. The scanner main body further presents at least any one of the moving direction or moving speed of the depth sensor to a user based on an evaluation result.