LIDAR Point Cloud Processing Using Point Indices
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Solution Overview
Problem
Conventional LIDAR systems face challenges in processing high frame rates and increased scanning points due to limited processing capability, power, and space, leading to increased processing time and power consumption.
Innovation Solution
The system uses point indices to process data points in LIDAR systems, allowing for efficient modification and generation of new data points without modifying existing data in memory, thereby reducing memory operations and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of scanning points and frame rate are increased to improve measurement coverage and resolution, then the productivity and measurement precision are improved, but the processing time and power consumption increase
Solution Approach 1:
The patent uses point indices to create references to existing data points in memory rather than copying or regenerating the actual point data. When a point needs to be processed or referenced, the system uses the index to access the original data point, avoiding redundant memory operations and reducing processing time while maintaining the ability to handle high frame rates and increased scanning points
2Measurement precision
If the number of scanning points is increased to improve measurement precision, then the productivity is improved, but the power consumption increases
Solution Approach 1:
The system stores point data in memory and uses indices to reference these pre-stored points during processing. This approach eliminates the need to repeatedly read, process, or regenerate the same point data, significantly reducing the computational load and power consumption while maintaining high measurement precision through the use of multiple scanning points
Solution Approach 2:
The patent performs preliminary actions by storing raw LIDAR data points in memory before processing. By pre-storing the data and creating indices to access it, the system avoids repeated processing of the same data, thereby reducing real-time power consumption while maintaining the ability to handle high-precision measurements with increased scanning points
3Measurement precision
If multiple processing operations are performed on data points to improve measurement accuracy, then the measurement precision is improved, but the processing time increases
Solution Approach 1:
The patent applies multiple processing operations by referencing the same original data points through different indices rather than creating multiple copies of the data. This allows the system to perform multiple comparisons and analyses (such as signal-to-noise ratio calculations, intensity threshold checks, and spatial location validations) on the same underlying data, improving measurement precision while minimizing processing time through efficient memory access patterns
Data Source
AI summary
A light detection and ranging (LIDAR) system includes a processor and a memory. The memory stores a plurality of data points and stores instructions that cause the LIDAR system to: generate the plurality of data points associated with one or more return beams corresponding to one or more optical beams transmitted towards a target; perform a plurality of processing operations on the plurality of data points to generate a point cloud corresponding to the target, wherein a first processing operation of the plurality of processing operations is configured to output a pair of indices as input to a second processing operation of the plurality of processing operations, the pair of indices referring to memory locations of a first data point and a second data point of the plurality of data points, respectively; and calculate a range and a velocity of the target based on the point cloud.