Overhead View Mapping Pipeline Optimization
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Solution Overview
Problem
Traditional overhead view mapping techniques using aerial photography and satellite imagery fail to accurately capture ground topography and detail due to occlusions and geometric distortions, resulting in inaccurate and incomplete map data, particularly for applications requiring high resolution and precision like autonomous vehicles.
Innovation Solution
A method involving the generation of aggregated overhead view images using ground-level source images, which are filtered and combined to minimize errors through reprojection and iterative optimization, allowing for accurate representation of ground elevation and occlusion removal, utilizing segmentation masks and image quality metrics to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite or aerial images are used to generate overhead view maps, then coverage area is improved, but image accuracy and detail quality deteriorate due to occlusions and geometric distortions
Solution Approach 1:
The patent segments the overhead view image generation process into multiple contributions from different source images. Each source image contributes to specific regions where it provides the best view, determined by evaluating visibility and occlusion levels. This segmentation allows the system to combine multiple partial views into a complete accurate overhead view.
Solution Approach 2:
The patent transitions from two-dimensional flat image composition to three-dimensional spatial reasoning by using depth information and camera pose data. Source images are reprojected into 3D space, allowing the system to determine which regions are visible from which camera positions, thereby resolving occlusions through spatial dimensionality.
2Measurement precision
If ground-level source images are combined to generate overhead view images, then image resolution and detail are improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing source images to extract key features, camera poses, and depth information before the actual overhead view generation. Source images are evaluated in advance to determine their contribution regions, and reprojection matrices are pre-calculated, reducing the computational burden during the final composition stage.
Solution Approach 2:
The patent creates reprojection copies of source images transformed into overhead view coordinates. Instead of directly processing and comparing all source images in their original form, the system generates reprojected versions that can be efficiently combined, reducing the complexity of the aggregation process.
3Ease of manufacture
If traditional aerial photography methods are used, then equipment cost is reduced, but image quality and occlusion handling deteriorate
Solution Approach 1:
The patent merges multiple ground-level source images taken from different positions and angles to create a composite overhead view that eliminates occlusions. By combining contributions from multiple cameras or multiple images from the same camera, the system achieves reliable occlusion handling using affordable ground-level equipment.
Solution Approach 2:
The patent introduces computational processing as an intermediary between ground-level image capture and overhead view generation. This computational mediator evaluates visibility, determines contribution regions, and reprojections images to synthesize an overhead view that ground-level cameras alone cannot directly capture.
4Area of stationary object
If satellite imagery is used for mapping, then large area coverage is achieved, but geometric distortion and measurement accuracy worsen
Solution Approach 1:
The patent changes the parameters of the imaging system by using ground-level cameras with known poses and intrinsic parameters instead of satellite cameras. By precisely controlling and knowing the camera parameters (position, orientation, focal length), the system achieves better geometric accuracy while maintaining large area coverage through multi-image aggregation.
Data Source
AI summary
Examples disclosed herein may involve (i) obtaining an aggregated overhead view image of a geographical area that has been generated by a pipeline for generating aggregated overhead view images, the geographical area comprising a plurality of regions, where the aggregated overhead view image is generated from aggregating pixel values from a plurality of source images of the geographical area, (ii) generating one or more reprojection images of one or more of the regions of the geographic area from the aggregated overhead view image, (iii) identifying, from the plurality of source images, one or more source images that capture the one or more regions of the geographical area, (iv) calculating one or more differences between the identified one or more source images and the one or more reprojection images, and (v) determining one or more error corrections to be applied to the pipeline for generating overhead view images.