Multi-Resolution Voxel Maps for Accurate Vehicle Localization
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Solution Overview
Problem
Existing map data systems for navigating vehicles in physical environments are inefficient in representing and aligning data at varying resolutions, leading to inaccuracies in localization and increased computational costs.
Innovation Solution
The use of a multi-resolution voxel space that stores spatial means, covariances, and weights of point distributions, allowing for the representation of environments at different resolutions and facilitating efficient alignment of data through voxel merging and alignment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional map data systems are used to represent environments at varying resolutions, then data storage and processing become simpler, but localization accuracy decreases and computational costs increase
Solution Approach 1:
The patent segments the environment representation into a multi-resolution voxel hierarchy, where the spatial space is divided into voxels at different resolution levels. Each voxel stores statistical parameters (mean, covariance, weight) of point distributions, enabling efficient representation and processing of environmental data at varying degrees of detail while maintaining high localization accuracy where needed.
2Measurement precision
If high-resolution data is used throughout the entire environment, then localization accuracy improves, but data storage requirements and processing time increase
Solution Approach 1:
The patent applies local quality by assigning different resolution levels to different regions of the environment based on their importance and proximity to the vehicle. High-resolution voxels are used in regions requiring precise localization (near the vehicle), while lower-resolution voxels are used in distant or less critical regions, optimizing the balance between accuracy and storage efficiency.
Solution Approach 2:
The patent introduces a resolution dimension to the traditional spatial representation, creating a multi-resolution voxel hierarchy. This additional dimension allows the system to efficiently store and process environmental data by selecting appropriate resolution levels for different computational tasks, reducing overall data storage requirements while maintaining necessary accuracy.
3Productivity
If multi-resolution voxel spaces are implemented, then data processing efficiency and localization accuracy improve, but system complexity increases
Solution Approach 1:
The patent changes the parameters stored in each voxel from raw point cloud data to statistical parameters (mean, covariance, weight). This parameter transformation enables efficient computation and comparison of voxels at different resolution levels, improving data processing efficiency while managing system complexity through mathematical abstraction.
Solution Approach 2:
The patent creates a simplified copy of the environment in the form of a multi-resolution voxel grid, where complex point cloud data is represented by statistical parameters. This copying approach allows efficient processing and alignment operations while reducing the complexity of directly handling raw sensor data.
Data Source
AI summary
Techniques for representing a scene or map based on statistical data of captured environmental data are discussed herein. In some cases, the data (such as covariance data, mean data, or the like) may be stored as a multi-resolution voxel space that includes a plurality of semantic layers. In some instances, individual semantic layers may include multiple voxel grids having differing resolutions. Multiple multi-resolution voxel spaces may be merged to generate combined scenes based on detected voxel covariances at one or more resolutions.