Semantic 3D Mesh Decimation for Accurate Vehicle Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current map decimation techniques for vehicles navigating in environments do not effectively reduce memory requirements and computation resources while maintaining accuracy, especially in three-dimensional maps represented by polygons, which is crucial for localization and safety.
Innovation Solution
The technique involves decimating three-dimensional maps based on semantic information, where polygons are combined or maintained based on their semantic classifications, using operators and levels tailored to their contribution to localization, thereby reducing the map size while preserving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If map decimation is performed to reduce memory requirements and computation resources, then processing efficiency improves, but localization accuracy deteriorates
Solution Approach 1:
The patent applies different decimation levels to different regions of the map based on their semantic classification. Critical regions (roads, sidewalks, curbs) are preserved with higher detail while non-critical regions (buildings, vegetation, water) are decimated more aggressively. This resolves the contradiction by maintaining localization accuracy in critical areas while improving processing efficiency in non-critical areas.
Solution Approach 2:
The patent segments the map into multiple regions based on semantic classification (road, sidewalk, curb, building, vegetation, water). Each segment is then decimated independently with appropriate parameters. This segmentation allows the system to maintain high localization accuracy where needed while achieving overall processing efficiency improvements.
2Quantity of substance
If map decimation is performed to reduce memory requirements, then storage capacity improves, but map detail deteriorates
Solution Approach 1:
Different regions of the map are decimated to different levels of detail based on their semantic classification. Roads, sidewalks, and curbs maintain fine geometric detail for accurate vehicle localization, while buildings, vegetation, and water bodies are decimated more aggressively. This resolves the contradiction by optimizing memory usage while preserving map detail where it matters most for navigation safety.
Solution Approach 2:
The patent changes the decimation parameter (level of detail) based on the semantic classification of each map region. Critical regions use lower decimation parameters to preserve detail, while non-critical regions use higher decimation parameters to reduce memory requirements. This dynamic parameter adjustment resolves the contradiction between memory efficiency and detail preservation.
Data Source
AI summary
Techniques for decimating portions of a map of an environment are discussed herein. The environment can be represented by a three-dimensional (3D) map including a plurality of polygons and semantic information associated with the polygons. In some cases, decimation operations may be based on semantic information associated with the environment. Differing decimation operations and/or levels may be applied to polygons of different semantic classifications or differing contribution levels. Boundaries between regions having different semantic information can be preserved. Meshes can be decimated using different decimation operators or decimation levels and an accuracy of localizing can be compared using the various decimated meshes. An optimal mesh can be selected and sent to vehicles for localizing the vehicles in the environment.