Keypoint Model for Adaptive 3D Map Building
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Solution Overview
Problem
Conventional keypoint matching systems for 3D scene reconstruction rely on costly and resource-intensive LIDAR sensors, which are less accurate in adverse conditions, and do not adapt well to changing environments, while also requiring pre-defined keypoints that may not be optimized for specific tasks or environments.
Innovation Solution
A method and apparatus for using a monocular camera to perform 2D keypoint matching with a pre-built 3D map, training a keypoint model to select keypoints based on a given task, and iteratively updating keypoints for improved accuracy, leveraging depth-aware keypoint learning and self-supervised training to enhance 3D scene reconstruction and localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors are used for 3D scene reconstruction, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces LIDAR sensors with a monocular camera system to achieve 3D scene reconstruction. Instead of using active LIDAR hardware, the system uses a passive camera combined with deep learning models (Depth Anytime, Keypoint Anytime) to infer depth and reconstruct 3D scenes from 2D images, thereby reducing device complexity while maintaining reconstruction capability
Solution Approach 2:
The patent creates a computational copy of LIDAR functionality using neural networks. The Depth Anytime model generates depth maps and the Keypoint Anytime model extracts 3D keypoints from 2D camera images, effectively copying the 3D reconstruction function that would otherwise require physical LIDAR hardware
2Measurement precision
If LIDAR sensors are used for 3D scene reconstruction, then measurement precision is improved, but reliability deteriorates in adverse conditions
Solution Approach 1:
The patent substitutes the LIDAR-based mechanical/optical system with a camera-based computational vision system. The monocular camera combined with deep learning models (Depth Anytime, Keypoint Anytime) processes images to generate depth information, providing robustness in adverse conditions where LIDAR fails due to weather, lighting, or environmental factors
Solution Approach 2:
The system uses self-supervised learning and iterative refinement where the Keypoint Anytime model automatically selects and refines keypoints based on task requirements. The Depth Anytime model iteratively improves depth estimates by incorporating semantic information and spatial relationships, enabling the system to adapt to varying environmental conditions without external intervention
3Device complexity
If pre-defined keypoints are used for matching, then device complexity is reduced, but adaptability deteriorates to specific tasks or environments
Solution Approach 1:
The patent implements dynamic keypoint selection where the Keypoint Anytime model automatically identifies and selects relevant keypoints based on the specific task and environment. Instead of using fixed pre-defined keypoints, the system adapts its keypoint selection in real-time based on image content, semantic information, and task requirements, thereby achieving high adaptability without significant complexity increase
Solution Approach 2:
The system changes the parameters of keypoint representation by using learnable features and descriptors that are optimized for specific tasks. The Keypoint Anytime model adjusts keypoint properties such as semantic labels, spatial relationships, and visual descriptors based on the input image and task context, enabling flexible adaptation to different environments and applications
Data Source
AI summary
A method for labeling keypoints includes labeling, via a keypoint model, a first set of keypoints in a first image associated with a three-dimensional (3D) map of an environment, the first image having been captured during a first time period. The method also includes labeling, via the keypoint model, a second set of keypoints in a second image associated with the 3D map, the second image having been captured during a second time period. The method further includes updating, via the keypoint model, one of more of the first set of keypoints based on labeling the second set of keypoints.