3D LiDAR Global Localization via 2D Grid Projection
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Solution Overview
Problem
Existing global localization technologies face challenges in dynamic environments where object positions frequently change, requiring frequent map updates and high computational loads for real-time localization using 3D LiDAR scanners, limiting their applicability in fields like warehouses.
Innovation Solution
A method that generates a 2D grid map from 3D point cloud data using occupancy probabilities and particle filters to estimate the 6-DOF position of a vehicle, allowing real-time global localization without the need for new map updates, by partitioning the 3D space and assigning particle samples to specific areas for efficient matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D LiDAR scanner is used for global localization in dynamic environments, then localization accuracy is improved, but computational load increases
Solution Approach 1:
The patent divides the 3D space into multiple 3D unit spaces along the Z-axis, corresponding to 2D grid cells on the XY plane. This segmentation allows the system to process localization in discrete spatial units, reducing the overall computational burden while maintaining accuracy through localized matching operations.
Solution Approach 2:
The patent transforms the 3D localization problem into a 2D grid map representation by projecting 3D point cloud data onto a 2D XY plane. This dimensionality reduction simplifies the computational complexity of global localization while preserving the essential spatial relationships needed for accurate positioning.
2Reliability
If frequent map updates are performed in dynamic environments, then localization reliability is improved, but productivity decreases
Solution Approach 1:
The patent pre-partitions the 3D space into unit spaces and pre-processes the environment map into a 2D grid representation before localization operations. This preliminary structuring allows the system to perform rapid localization queries without requiring frequent full-map updates, maintaining reliability while improving operational efficiency.
3Measurement precision
If 3D point cloud data is processed in full detail, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent segments the dense 3D point cloud data into discrete 3D unit spaces that map to 2D grid cells. This segmentation reduces the data volume requiring processing while preserving positional accuracy through the structured spatial representation, enabling faster processing without sacrificing measurement precision.
Solution Approach 2:
The patent projects detailed 3D point cloud information onto a 2D grid map, reducing the dimensionality of data processing. This transformation maintains the essential spatial information needed for accurate position estimation while dramatically reducing the computational time required for processing.
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
Enables real-time global localization in dynamic environments with improved accuracy and reduced computational load, ensuring stable autonomous navigation without the need for continuous map updates, thus enhancing the applicability of 3D LiDAR scanners in various navigation fields.
Implementation Method 1
3D Light Detection and Ranging (LiDAR) scanner
Data Source
AI summary
Disclosed herein are an apparatus and method for global localization for a dynamic environment using a 3D LiDAR scanner. The method may include generating a 2D grid map from 3D point cloud data acquired using the 3D LiDAR scanner, searching for the 2D global position of a vehicle on the 2D grid map using data acquired from the 3D LiDAR scanner, and mapping the 2D global position to a 6-DOF position in the 3D space.