Semantic Map Decimation for Autonomous Vehicle Localization
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Solution Overview
Problem
Existing map decimation techniques for environments, particularly in autonomous vehicle navigation, fail to efficiently reduce the size of three-dimensional maps while maintaining accuracy and utility, leading to increased memory requirements and computation resources.
Innovation Solution
The use of semantic information to selectively decimate polygons in three-dimensional maps, where regions contributing to vehicle localization are preserved at higher detail, and less contributing regions are decimated more aggressively, using various decimation operators and levels based on semantic classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If map decimation is applied to reduce three-dimensional map size, then memory requirements and computation resources are reduced, but localization accuracy may deteriorate
Solution Approach 1:
The patent applies different decimation levels to different semantic regions of the map. High-priority regions (roads, sidewalks, buildings) are preserved with fine detail (low decimation), while low-priority regions (vegetation, water bodies) are aggressively decimated. This local differentiation resolves the contradiction by maintaining localization accuracy in critical areas while reducing overall map size through selective simplification of non-critical areas.
Solution Approach 2:
The patent segments the three-dimensional map into multiple semantic regions based on classification labels (e.g., road, building, vegetation). Each segment is then independently decimated according to its semantic priority. This segmentation allows the system to preserve localization-critical features while removing redundant details, thereby reducing map size without compromising localization accuracy in priority regions.
2Productivity
If aggressive decimation is applied to reduce map size, then processing efficiency improves, but map detail and utility are lost
Solution Approach 1:
The patent implements variable decimation intensity across different map regions based on semantic classification. High-priority regions maintain fine geometric detail with conservative decimation ratios, while low-priority regions undergo aggressive decimation. This local quality approach ensures that processing efficiency gains from decimation do not come at the cost of losing critical map details needed for navigation and localization.
Solution Approach 2:
The patent dynamically adjusts decimation parameters (decimation ratio, mesh resolution) based on semantic region classification. Different parameter sets are applied to different region types: roads use low decimation ratios to preserve lane markings and geometry, while vegetation areas use high decimation ratios to reduce polygon count. This parameter adaptation resolves the contradiction by optimizing the balance between processing efficiency and detail preservation for each region type.
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.