LiDAR Point Cloud Processing for Low Reflectivity Object Length
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Solution Overview
Problem
LiDAR systems face challenges in accurately measuring the length of moving objects, particularly those with low reflectivity, such as black vehicles, due to reduced laser reflection, which limits the acquisition of their shape information.
Innovation Solution
A method involving a LiDAR system that acquires time-series point cloud information from multiple regions along a moving body's path, calculates velocity, front end, and rear end positions, and uses these to determine the length of the moving object, even when it hardly reflects laser light, by utilizing easily reflective surfaces like license plates or bumpers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR measures distance by receiving reflected light from the object, then measurement distance becomes longer when divergence angle of emitted laser light is small, but distance measurement accuracy decreases when the target object is difficult to reflect laser light
Solution Approach 1:
The patent introduces an information processing device that acts as an intermediary between the LiDAR measurement system and the final length calculation. This device processes point cloud data from multiple regions and times, calculates velocity based on temporal changes, and synthesizes front end and rear end positions to determine object length, thereby compensating for insufficient reflected light signals from low-reflectivity objects
Solution Approach 2:
The patent transitions from single-point or single-region distance measurement to multi-dimensional analysis by acquiring point cloud information from multiple regions (first, second, third regions) along the moving body path and multiple time points, then integrating these dimensional data to calculate length, effectively overcoming the limitation of poor reflectivity
2Area of stationary object
If LiDAR acquires shape information by scanning emitted laser light, then scanning range becomes wider when divergence angle of laser light is large, but measurement distance becomes shorter
Solution Approach 1:
The patent performs preliminary acquisition of point cloud information from multiple regions along the moving body path before calculating the final length. By pre-capturing data from the first region (upstream), second region (center), and third region (downstream), the system ensures sufficient measurement distance is maintained while gathering comprehensive spatial information for accurate length calculation
3Adaptability or versatility
If LiDAR uses multiple types with different divergence angles to cover various measurement environments, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent creates a universal information processing device that can handle measurements from a single LiDAR unit across diverse measurement environments. The device achieves multi-functionality by processing point cloud data from different regions and time points to calculate lengths of various moving objects, eliminating the need for multiple specialized LiDAR types with different divergence angles
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 measurement of moving objects' lengths, even those with low reflectivity, by leveraging easily reflective surfaces, thereby improving distance measurement accuracy and shape acquisition.
Implementation Method 1
The LiDAR measures a distance to an object by irradiating a detection target space with laser light and receiving reflected light from the object
Implementation Method 2
Light detection and ranging (LiDAR) (laser imaging detection and ranging) is used to obtain shape information of an object
Data Source
AI summary
The length of a moving body made of a material that hardly reflects laser light is measured with high accuracy.First point cloud information based on three-dimensional point cloud information of a first region A1 of a moving body path RW in which a movement direction is set, second point cloud information based on three-dimensional point cloud information of a second region A2, and third point cloud information based on three-dimensional point cloud information of a third region A3 downstream of the second region A2 are acquired in a time series. A velocity VAM of a moving body AM is calculated based on a temporal change of the first point cloud information A1. A front end position FE of the moving body AM at a first time T1 is calculated based on the second point cloud information A2. A rear end position RE of the moving body AM at a second time T2 is calculated based on the third point cloud information A3. A length LAM of the moving body is calculated based on the velocity VAM of the moving body, the front end position FE of the moving body AM at the first time T1, and the rear end position RE of the moving body AM at the second time T2.