Multi-Resolution Voxel Maps for Vehicle Localization
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Solution Overview
Problem
Existing map representation technologies, such as those using polygons and meshes, are inefficient for accurate vehicle localization and data processing in dynamic environments, as they lack detailed resolution and require extensive computational resources.
Innovation Solution
The use of a multi-resolution voxel space that stores spatial data, covariances, and weights of point distributions, allowing for the representation of environments at varying resolutions and facilitating alignment of sensor data with semantic layers, enabling more accurate and efficient localization and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-resolution voxel space is used to represent environments, then measurement precision and localization accuracy are improved, but device complexity and computational resources increase
Solution Approach 1:
The environment representation is segmented into multiple resolution levels (coarse to fine), where each level handles different spatial frequencies. Coarse levels capture global structure with low computational cost, while fine levels provide detailed localization information only where needed, resolving the contradiction between precision and complexity.
Solution Approach 2:
Different regions of the environment are represented at different resolutions based on their importance and dynamic characteristics. High-motion or critical localization areas use fine resolution voxels, while static or less important areas use coarse resolution, optimizing the balance between accuracy and computational load.
2Measurement precision
If high-resolution voxel data is stored for detailed environmental representation, then measurement precision is improved, but loss of time and processing speed worsen
Solution Approach 1:
The system dynamically selects and processes only the necessary resolution levels based on current operational needs, vehicle position, and environmental dynamics. This adaptive approach ensures high precision when required while maintaining fast processing during normal operations, resolving the time-loss contradiction.
Solution Approach 2:
Coarse-resolution voxel data is pre-processed and stored in advance to provide rapid global context, while fine-resolution details are loaded and processed only when needed for precise localization tasks, reducing overall processing time while maintaining detail quality.
3Manufacturing precision
If multi-resolution voxel grids are merged and aligned, then manufacturing precision and data accuracy are improved, but device complexity and alignment computation increase
Solution Approach 1:
The alignment process is extended into the resolution dimension, where grids are aligned not only in spatial coordinates (x, y, z) but also across multiple resolution levels. This multi-dimensional alignment approach ensures data accuracy by coordinating transformations across all levels simultaneously, while the hierarchical structure reduces the overall computational burden compared to aligning all details at once.
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.