Flash Lidar Point Cloud De-jitter via Entropy Minimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Jitter in point cloud data from flash lidar systems introduces errors that complicate target recognition, stemming from the relative motion of components within the system.
Innovation Solution
The method involves processing groups of data points in succession to minimize entropy by repetitively shifting data points in the spatial coordinate system and recalculating entropy until it reaches a minimum, effectively de-jittering the point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flash lidar systems capture point cloud data using a grid of sensors, then 3-D target representation is generated, but jitter occurs due to relative motion of components
Solution Approach 1:
The patent segments the point cloud data into multiple groups based on temporal or spatial characteristics. Each group is processed independently through entropy minimization to remove jitter, allowing localized optimization without affecting the entire dataset. This segmentation approach enables precise jitter removal while maintaining overall data integrity.
Solution Approach 2:
The patent changes the spatial parameters of data points by applying transformations (such as coordinate system changes or spatial shifts) to minimize entropy within each group. By adjusting these parameters iteratively, the system removes jitter while preserving the underlying target structure, thus improving measurement precision without sacrificing reliability.
2Measurement precision
If data points are processed to remove jitter, then target recognition accuracy improves, but processing time increases
Solution Approach 1:
The patent divides the point cloud data into multiple groups that can be processed in parallel or sequentially with reduced computational overhead. By processing smaller subsets rather than the entire dataset at once, the system achieves jitter removal with lower computational complexity, thus reducing processing time while maintaining target recognition accuracy.
Solution Approach 2:
The patent applies entropy minimization processing selectively to specific groups of data points rather than uniformly to all points. This partial action approach focuses computational resources on regions where jitter removal provides the most benefit, optimizing the balance between processing time and target recognition accuracy.
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 reduces jitter in point cloud data, enhancing the accuracy of target recognition processes by minimizing data disorder and improving correlation between data points.
Implementation Method 1
Lidar (light detection and ranging) is a process that measures distances to a target by illuminating the target with laser light and measuring the reflected light with one or more sensors. Differences in the laser return times and/or wavelengths can then be used to make a digital 3-D representation of the target.
Implementation Method 2
detecting laser light reflected from the target utilizing a planar array of Geiger-mode avalanche photodiodes (APDs)
Data Source
AI summary
Jitter is removed from point cloud data by processing different groups of data points in the point cloud data in succession to minimize the entropy of the point cloud data. Each group in the point cloud data is generated at either different points in time or from detecting different pulses of reflected laser light from a target. The data points in a selected group are repetitively shifted en masse in the coordinate system of the point cloud data and the entropy of the point cloud data is re-calculated until subsequent shifts of the data points in the selected group does not further reduce the entropy. The remaining groups are subsequently processed in a similar fashion until the entropy of the point cloud data reaches a minimum.