Model-Based Scan Line Encoder for LIDAR Data Compression
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Solution Overview
Problem
Existing LIDAR scanning technologies face inefficiencies due to mechanical misalignments and offsets, leading to large deviations in scan data conversion and inefficiencies in naive encoding techniques, resulting in artifacts like voids in scan lines.
Innovation Solution
A model-based scan line encoding method is introduced, which defines a geometry model for describing scan lines, calculates a trajectory model to represent deviations, and compresses residuals to enable effective lossless and lossy compression, matching data loss with native error sources, and allows quick location of scan data without uncompressing entire datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If naive encoding techniques are used to store scan data, then the implementation is simple, but the storage space required is large and artifacts like voids appear in scan lines
Solution Approach 1:
The scan data is segmented into scan lines, and each scan line is further segmented into groups of points. A separate encoding model is applied to each scan line, allowing independent optimization and reducing the overall storage requirements while maintaining simplicity in implementation.
Solution Approach 2:
The patent transforms the scan data from raw coordinate format into a parametric representation using polynomial models. By changing the parameters from storing all point coordinates to storing model coefficients and deviation values, the storage space is significantly reduced while eliminating artifacts like voids.
2Area of stationary object
If mechanical positioning is used to scan the LIDAR, then the system can cover a large area, but misalignments and offsets cause large deviations in scan data
Solution Approach 1:
The patent incorporates feedback by calculating the deviation of actual scan points from the polynomial trajectory model and storing these residuals. This feedback mechanism allows the system to compensate for mechanical misalignments and offsets by correcting the idealized model with measured deviations, thereby improving scan data accuracy while maintaining large coverage area.
Solution Approach 2:
The patent performs preliminary action by establishing a polynomial trajectory model that represents the ideal scan path before processing individual scan points. This pre-established model serves as a reference framework that accounts for expected mechanical behavior, allowing subsequent deviations to be measured and corrected systematically.
3Loss of information
If entire datasets are uncompressed for access, then all data is available, but the access time is slow and storage space is wasted
Solution Approach 1:
The patent segments the compressed data structure into scan line-level units that can be independently accessed and decompressed. This allows the system to load and process only the specific scan lines needed for a particular query, rather than uncompressing the entire dataset, thereby reducing access time and preserving data availability for selective access.
Data Source
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AI summary
A model-based scan line encoder is disclosed. A method of model-based scan line encoding includes defining a geometry model for describing a scan line of a scan, the scan line including multiple scan points. The method further includes calculating a trajectory model representing an approximate pattern of deviation of the multiple scan points relative to the geometry model. The method further includes calculating multiple residuals, each of the residuals associated with a difference between the deviation of the scan points and the trajectory model. The method may further include compressing the residuals.