Covariance Voxel Mapping for Multi-Resolution Vehicle Localization
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Solution Overview
Problem
Existing map data representation methods, such as those using polygons and meshes, are inefficient for accurate vehicle localization and environment understanding, particularly in autonomous navigation, as they lack detailed resolution and require extensive processing resources.
Innovation Solution
The use of a multi-resolution voxel space that stores spatial data, covariances, and weights of point distributions, allowing for the generation of voxel grids at different resolutions, which are aligned to create a detailed and efficient representation of the environment for improved localization and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional polygon or mesh representations are used for environment maps, then the map can represent the environment, but the processing resources required are extensive and localization accuracy is insufficient
Solution Approach 1:
The environment map is segmented into a three-dimensional voxel grid structure, dividing the continuous space into discrete volumetric elements. Each voxel can independently store localization data, allowing parallel processing and reducing computational complexity while maintaining high localization accuracy through precise spatial discretization
Solution Approach 2:
The patent transitions from traditional two-dimensional polygon/mesh representations to a three-dimensional voxel grid representation. This dimensional expansion enables more accurate spatial localization by capturing depth information and creating a volumetric map structure that better represents the physical environment, thereby improving localization accuracy without proportionally increasing processing resources
2Measurement precision
If high-resolution map data is used to improve localization accuracy, then more detailed environment representation is achieved, but processing requirements increase
Solution Approach 1:
The voxel grid resolution is made dynamic and adaptive, allowing different regions of the environment to be represented at different resolutions based on their importance and the vehicle's operational context. High-resolution voxels are allocated to critical areas requiring precise localization, while less critical areas use lower resolution, thereby maintaining high localization accuracy where needed while optimizing overall processing efficiency
Solution Approach 2:
The patent changes the resolution parameter of the voxel grid dynamically, adjusting the level of detail in different spatial regions. By varying the voxel size and density based on environmental complexity and localization requirements, the system achieves high localization accuracy in critical areas while reducing processing requirements in less critical areas, thus resolving the contradiction between precision and productivity
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.