LiDAR Imaging Condition Planning for Clear Point Cloud Capture
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Solution Overview
Problem
Existing LiDAR technologies do not provide a method for determining appropriate imaging conditions, such as position, posture, and angle, which affect the quality of point cloud data acquisition due to variations in distance, incident angle, and shielding by objects.
Innovation Solution
An imaging condition determination method and system that utilizes reference data and indication objects to determine optimal imaging conditions for LiDAR, including calculating imaging regions, positions, and postures based on these data to ensure clear and effective imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR imaging is performed without determining optimal imaging conditions, then imaging can be conducted freely, but the quality and accuracy of acquired point cloud data deteriorates due to variations in distance, incident angle, and shielding
Solution Approach 1:
The system performs preliminary determination of imaging conditions (position, posture, angle) before actual LiDAR imaging. By calculating optimal imaging regions and parameters in advance based on facility data and target object information, the system ensures high-quality point cloud acquisition without requiring complex real-time adjustments during imaging.
Solution Approach 2:
The system uses facility data (digital twin or CAD model) as a copy or representation of the actual facility structure. This digital copy allows pre-calculation of imaging conditions and visualization of imaging regions without requiring physical trial-and-error imaging, thereby improving measurement precision while avoiding the complexity of repeated physical adjustments.
2Measurement precision
If multiple imaging positions and postures are evaluated to determine optimal imaging conditions, then imaging accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The system replaces physical trial-and-error imaging with computational calculations. By using facility data and target object information to calculate optimal imaging conditions mathematically, the system evaluates multiple positions and postures virtually without the time cost of actual imaging trials, thereby improving imaging accuracy while minimizing time loss.
Solution Approach 2:
The system performs preliminary calculation of imaging regions and optimal conditions before actual imaging. By pre-determining the best imaging positions and postures based on digital facility models, the system avoids time-consuming trial imaging while ensuring high measurement precision in the actual imaging process.
3Loss of information
If imaging is performed from various positions to capture complete target object data, then data completeness improves, but the complexity of positioning and coordination increases
Solution Approach 1:
The system uses a universal facility data model that represents the entire facility structure and all target objects. This single digital model serves multiple functions: it enables calculation of imaging regions, determination of optimal positions, and coordination of imaging sequences for all target objects, thereby ensuring data completeness without requiring separate complex positioning systems for each object.
Solution Approach 2:
The system introduces facility data (digital twin or CAD model) as an intermediary between the LiDAR system and the physical facility. This intermediary contains all spatial and structural information, enabling the system to calculate complete imaging coverage and coordinate multiple imaging positions systematically, thereby ensuring data completeness while reducing positioning complexity.
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
Enables the determination of appropriate imaging conditions for LiDAR, ensuring accurate and unobstructed data acquisition by identifying optimal positions and postures for the LiDAR system.
Implementation Method 1
An example of a method of measuring the distance to the target object by the LiDAR includes a time of flight (ToF) method
Implementation Method 2
the point cloud data acquired by the LiDAR includes not only the data of the three-dimensional coordinates of the measurement point on the target object but also data of reflection luminance of the measurement point (intensity of reflected light at the measurement point)
Data Source
AI summary
An imaging condition determination system according to the present disclosure includes: a reference data holding unit (101) configured to hold reference data representing a position of an object; an indication object holding unit (102) configured to hold an indication object representing a position of a target object on the reference data; an imaging condition determination unit (103) configured to determine an imaging condition of LiDAR for imaging the target object based on the reference data and the indication object; and an imaging condition output unit (104) configured to output the imaging condition.