Scan Data Clustering for Vehicle Trajectory Correction
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Solution Overview
Problem
Current vehicle/robot localization methods face challenges in accurately optimizing scan data and correcting trajectory due to inconsistencies in sensor data from odometry, GPS, laser scanners, and cameras, particularly in matching laser points with grid maps or point cloud maps.
Innovation Solution
A method and apparatus for optimizing scan data by clustering data points into map elements, establishing correspondence between frames, and optimizing clusters to correct the vehicle/robot trajectory, using Gaussian Mixture Models to simulate environmental shapes and descriptors to improve matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser points are matched with grid maps or point cloud maps for localization, then vehicle/robot positioning can be achieved, but matching accuracy is insufficient due to sensor data inconsistencies
Solution Approach 1:
The patent combines multiple sensor data sources (odometry, GPS, laser scanner, camera) into a unified scan data representation using Gaussian Mixture Models. By merging these diverse sensor inputs and representing them as clustered point clouds with descriptors, the system achieves more reliable and accurate localization than individual sensor matching could provide.
2Measurement precision
If multiple sensor data sources are integrated for localization, then positioning capability is enhanced, but data processing complexity increases
Solution Approach 1:
The patent transforms complex multi-sensor data into a standardized parameter representation using Gaussian Mixture Models with specific parameters (mean, covariance, weight). By changing the data representation parameters to this unified mathematical form, the system simplifies processing while maintaining the rich information from multiple sensors.
Solution Approach 2:
The patent creates simplified copies of complex sensor data in the form of clustered point representations with descriptors. Instead of processing raw multi-sensor data directly, the system creates condensed cluster models that capture essential features, reducing processing complexity while preserving localization capability.
3Measurement precision
If cluster optimization calculations are performed on correspondence sets, then scan data accuracy is improved, but computational time increases
Solution Approach 1:
The patent segments the optimization problem by processing correspondence sets of clusters in discrete, manageable units. Instead of optimizing all scan data points simultaneously, the system divides them into clusters and processes correspondence relationships between clusters, making the computational task more efficient while maintaining accuracy.
Data Source
AI summary
A method and an apparatus optimizes scan data obtained by sensors on vehicle, and corrects trajectory for a vehicle/robot based on the optimized scan data. The method for optimizing the scan data obtained by scanning environment elements, includes: step of obtaining the scan data, including obtaining at least two frames of scan data respectively corresponding to different timings; step of cluster processing, based on the characteristic of the data points, including classifying the plurality of data points in each frame of the scan data into one or more clusters; step of establishing correspondence, among the at least two frames of scan data, including searching and obtaining at least one set of clusters having correspondence; step of optimizing clusters, among the at least two frames of scan data, including conducting calculation to each set of the at least one set of clusters having correspondence, to obtain optimized clusters respectively corresponding to each set of the at least one set of clusters having correspondence; and step of optimizing the scan data, including accumulating all optimized clusters to obtain an optimized scan date for the at least two frames of scan data.