3D Visual Servoing Robot Positioning via Point Cloud Conversion
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Solution Overview
Problem
Current robotic visual servoing techniques using two-dimensional scanning face accuracy issues due to a lack of depth information, making it difficult to accurately position robots in environments with complex structures and dark conditions.
Innovation Solution
Implementing three-dimensional visual servoing by obtaining point cloud data, converting it into two-dimensional images, and using image processing techniques to identify the three-dimensional position of features, which is then provided to a controller for precise robot positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two-dimensional scanning is used for visual servoing, then the processing speed is fast and data amount is manageable, but the positioning accuracy deteriorates due to lack of depth information
Solution Approach 1:
The patent converts three-dimensional point cloud data into two-dimensional images for processing, while preserving depth information through intensity values. This allows the system to use efficient 2D image processing techniques while maintaining 3D positioning accuracy, resolving the contradiction between measurement precision and device complexity.
2Reliability
If two-dimensional cameras are used, then the device complexity is low, but the ability to detect features in complex structures and dark conditions deteriorates
Solution Approach 1:
The patent uses a three-dimensional camera as an intermediary device that captures both spatial and intensity information. The conversion of point cloud data to images with intensity values serves as a mediator that preserves depth information while enabling standard image processing techniques, thereby improving feature detection reliability without excessive system complexity.
3Productivity
If three-dimensional point cloud data is processed directly, then the positioning accuracy is high, but the processing time increases and productivity decreases
Solution Approach 1:
The patent extracts essential depth information from three-dimensional point cloud data and encodes it into intensity values of two-dimensional images. This extraction process removes unnecessary data while preserving the critical depth information needed for accurate positioning, thereby improving processing speed without sacrificing measurement precision.
Data Source
AI summary
Three-dimensional visual servoing for positioning a robot in an environment is facilitated. Three-dimensional point cloud data of a scene of the environment is obtained, the scene including a feature. The three-dimensional point cloud data is converted into a two-dimensional image, and a three-dimensional position of the feature is identified based on the two-dimensional image. An indication of the identified three-dimensional position of the feature is then provided.