Multi-Resolution Voxel Maps Using Covariances for Fast 3D Localization
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Solution Overview
Problem
Existing map representations for vehicles, particularly in three-dimensional environments, are inefficient in terms of computational resources and processing speed, and lack detailed spatial information necessary for accurate localization and navigation.
Innovation Solution
The use of multi-resolution voxel spaces that store spatial means, covariances, and weights of point distributions, allowing for the generation of maps with varying resolutions and semantic information, enabling efficient alignment and localization of vehicles by merging voxels and performing principal component analysis on covariance matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional map representations are used for three-dimensional environments, then the system can store spatial information, but the computational resources and processing speed are inefficient
Solution Approach 1:
The patent segments the three-dimensional space into discrete volumetric elements called voxels, organizing spatial data into a grid structure. This segmentation allows for efficient storage and processing by dividing the continuous environment into manageable discrete units, enabling faster computational operations compared to traditional continuous spatial representations.
Solution Approach 2:
The patent transitions from two-dimensional map representations to three-dimensional voxel-based representations, adding a vertical dimension to spatial modeling. This dimensional enhancement provides more comprehensive spatial information for navigation while the voxel discretization maintains computational efficiency through regular grid structures that optimize memory access and processing.
2Measurement precision
If detailed spatial information is stored in map representations, then accurate localization and navigation are enabled, but the data size and processing complexity increase
Solution Approach 1:
The patent applies different resolution levels to different regions of the three-dimensional space, creating a multi-resolution voxel structure. Areas requiring high localization precision are represented with finer voxel grids, while distant or less critical areas use coarser resolution. This local quality differentiation maintains high measurement precision where needed while reducing overall data size and processing complexity.
Solution Approach 2:
The patent changes the resolution parameter of the voxel grid dynamically, allowing adjustment between fine and coarse representations. By modifying the voxel size parameter, the system can adapt the level of detail to match the required localization accuracy for different operational contexts, balancing precision requirements against computational complexity.
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.