Virtual Beam Segmentation for Lidar Edge and Planar Point Detection
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Solution Overview
Problem
Existing lidar systems struggle to efficiently identify edge and planar points in lidar point clouds, particularly in non-beam-based systems where traditional feature extraction techniques are unsuitable.
Innovation Solution
The use of virtual beams to artificially divide the lidar point cloud into horizontal strips, allowing existing feature extraction methods like LOAM to identify edge and planar points within each virtual beam.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature extraction techniques are used in non-beam-based lidar systems, then the system cannot identify edge and planar points effectively, but the patent applies virtual beams to enable feature extraction
Solution Approach 1:
The patent introduces virtual beams as an intermediary structure that bridges non-beam-based lidar systems and traditional feature extraction methods. By projecting virtual beam structures onto the point cloud data from non-beam-based systems, the patent enables these systems to utilize established edge and planar point identification algorithms that were originally designed for beam-based systems, thereby achieving both improved measurement precision and broad system adaptability
2Measurement precision
If the point cloud is processed without virtual beams, then the processing is simpler, but the ability to identify edge and planar points is reduced
Solution Approach 1:
The patent segments the point cloud processing task by introducing virtual beams that divide the three-dimensional point cloud into multiple two-dimensional projection planes. This segmentation transforms the complex 3D feature extraction problem into multiple simpler 2D feature extraction problems that can be solved using established algorithms, thereby improving measurement precision while managing processing complexity through problem decomposition
3Measurement precision
If virtual beams are introduced to divide the point cloud, then edge and planar point identification is improved, but the processing time increases
Solution Approach 1:
The virtual beam segmentation enables parallel processing of different projection planes, where each virtual beam's projection can be processed independently. This segmentation strategy improves measurement precision by enabling comprehensive feature detection while managing processing time through parallel computation across multiple independent virtual beam projections
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 enhances the ability to identify edge and planar points in both beam-based and non-beam-based lidar systems, improving the accuracy of object detection and vehicle navigation systems.
Implementation Method 1
a lidar system to transmit incident light and receive reflections from one or more objects as a point cloud of points
Data Source
AI summary
A system in a vehicle includes a lidar system to transmit incident light and receive reflections from one or more objects as a point cloud of points. The system also includes processing circuitry to identify planar points and to identify edge points of the point cloud. Each set of planar points forms a linear pattern and each edge point is between two sets of planar points, and the processing circuitry identifies each point of the points of the point cloud as being within a virtual beam among a set of virtual beams. Each virtual beam of the set of virtual beams representing a horizontal band of the point cloud.