Semantic Keypoint Matching for Camera-Based 2D-3D Localization
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Solution Overview
Problem
Conventional keypoint matching systems face challenges in accurately localizing 2D keypoints from images to 3D maps due to the limitations of costly and resource-intensive LIDAR sensors, which are also less accurate in adverse environmental conditions, and struggle with training due to inconsistencies in defining interest points and limited learning areas from small patches.
Innovation Solution
The system augments keypoint descriptors with semantic information using a pre-trained segmentation network, enabling more robust 2D-3D keypoint matching by embedding semantic features into the descriptor, allowing for improved localization and mapping functions, particularly by using cameras instead of LIDAR sensors for 2D keypoint matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors are used for keypoint matching, then measurement precision is improved, but cost and resource consumption increase
Solution Approach 1:
The patent replaces LIDAR sensors with camera-based vision systems for keypoint matching. Instead of using active light detection and ranging technology, the system uses passive optical capture followed by computer vision processing to achieve 2D-3D keypoint correspondence, significantly reducing hardware cost and resource consumption while maintaining matching accuracy
Solution Approach 2:
The patent creates 2D keypoint descriptors from 3D map data and compares them with 2D image keypoints. This copying approach allows the system to leverage existing 3D map information without requiring additional LIDAR scanning, reducing resource consumption while maintaining measurement precision through semantic-aware matching
2Device complexity
If conventional feature matching is used, then device complexity is reduced, but reliability deteriorates in adverse environmental conditions
Solution Approach 1:
The patent transforms conventional 2D-2D feature matching into 2D-3D semantic-aware keypoint matching by incorporating depth and semantic information from pre-trained segmentation networks. This parameter enhancement allows the system to maintain reliability in adverse conditions while keeping the camera-based hardware relatively simple
Solution Approach 2:
The patent introduces pre-trained segmentation networks as intermediary components that provide semantic guidance to the keypoint matching process. These networks act as mediators between the simple camera input and the robust matching output, enhancing reliability without significantly increasing overall system complexity
3Quantity of substance
If small patches are used for training, then training data requirements are reduced, but manufacturing precision of keypoint detection deteriorates
Solution Approach 1:
The patent transitions from 2D patch-based training to 3D-aware training by incorporating depth information and semantic labels from pre-trained segmentation networks. This dimensional enhancement allows the system to achieve higher keypoint detection precision while still using limited training data, as the semantic information provides additional constraints and guidance
Data Source
AI summary
A method for keypoint matching performed by a semantically aware keypoint matching model includes generating a semanticly segmented image from an image captured by a sensor of an agent, the semanticly segmented image associating a respective semantic label with each pixel of a group of pixels associated with the image. The method also includes generating a set of augmented keypoint descriptors by augmenting, for each keypoint of the set of keypoints associated with the image, a keypoint descriptor with semantic information associated with one or more pixels, of the semantically segmented image, corresponding to the keypoint. The method further includes controlling an action of the agent in accordance with identifying a target image having one or more first augmented keypoint descriptors that match one or more second augmented keypoint descriptors of the set of augmented keypoint descriptors.