LiDAR Detector Group Sampling for Higher Point Cloud Density
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Solution Overview
Problem
Existing LiDAR systems face challenges in increasing point cloud density without enlarging the physical size or increasing costs, as current methods often result in complex optical-mechanical structures and inefficient signal detection.
Innovation Solution
The LiDAR system employs multiple emitter and detector units with one-to-one correspondence, each comprising multiple detector groups, allowing for histogram sampling and combining of sampling results to determine mixed and net channel data, thereby enhancing point cloud density without altering the optical-mechanical structure or energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of detector units is increased to improve point cloud density, then the point cloud density is improved, but the physical size and cost of the LiDAR increase
Solution Approach 1:
Each detector unit is divided into multiple detector groups, allowing independent sampling and processing. This segmentation enables the system to achieve higher point cloud density through multiple sampling results from different detector groups without physically increasing the number of detector units, thus avoiding the associated increase in physical size and cost.
Solution Approach 2:
The patent introduces a virtual dimension by combining sampling results from multiple detector groups to generate mixed channel data. This virtual channel approach allows the system to effectively increase detection capacity and point cloud density without adding physical detector units, thereby avoiding the volume increase that would result from physically adding more detectors.
2Measurement precision
If the number of detector units is increased to improve point cloud density, then the point cloud density is improved, but the cost of the LiDAR increases
Solution Approach 1:
By segmenting each detector unit into multiple detector groups, the system achieves higher point cloud density through software-based sampling and combination of results. This approach avoids the need to physically increase the number of detector units, thereby avoiding the associated cost increase that would result from manufacturing and integrating additional detector components.
Solution Approach 2:
The patent creates virtual copies of detection channels by combining sampling results from multiple detector groups. These virtual mixed channel data replicate the functionality of additional physical detector units without requiring actual hardware duplication, thus achieving improved point cloud density without the corresponding cost increase.
3Measurement precision
If the optical-mechanical structure is made more complex to increase point cloud density, then the point cloud density is improved, but the structure complexity increases
Solution Approach 1:
The patent replaces mechanical complexity with computational methods by using signal processing to combine sampling results from multiple detector groups. Instead of physically complexifying the optical-mechanical structure to achieve higher point cloud density, the system uses software-based algorithms to process and combine detection data, thereby improving point cloud density while maintaining simple mechanical structure.
4Area of stationary object
If the detection range is extended to cover more areas, then the detection coverage is improved, but the point cloud density in specific regions decreases
Solution Approach 1:
The patent segments the detection field into multiple virtual channels by combining sampling results from different detector groups. This segmentation allows the system to maintain high point cloud density in specific regions while extending detection coverage, as the mixed channel data from multiple detector groups provides enhanced resolution and density information across the entire detection area.
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 increases point cloud density and improves detection capacity for small signals without enlarging the system, maintaining the existing structure and energy efficiency, and effectively enhances detection capabilities.
Implementation Method 1
a detector module including multiple detector units, there being one-to-one correspondence between the multiple detector units and the multiple emitter units
Implementation Method 2
The LiDAR emits detection light into a three-dimensional space
Data Source
AI summary
Detection methods and apparatuses of a LiDAR are provided. An emitter module of the LiDAR includes multiple emitter units, a detector module of the LiDAR includes multiple detector units, the multiple detector units and the multiple emitter units have a one-to-one correspondence, the detector unit includes multiple detector groups. One detection method includes: sampling a signal outputted from each detector group, to determine a sampling result corresponding to each detector group; and determining a point cloud map based on sampling results corresponding to all of the detector groups.