Camera Pose Estimation via Motion-Adaptive Relocalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional camera pose estimation methods, such as the image-to-image and image-to-map techniques, face challenges in accurately estimating camera position and pose, especially when there are translational and rotational movements, leading to inefficient relocalization processing and increased processing costs.
Innovation Solution
A camera pose estimation device that determines the type of camera motion and selects the appropriate method (image-to-image or image-to-map) for relocalization processing, creating a three-dimensional map and keyframe table to estimate camera position and pose, using feature points and descriptors to match and calculate the camera's position and pose matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional camera pose estimation methods (image-to-image or image-to-map) are used, then camera position and pose can be estimated, but the processing becomes inefficient and costly when the camera undergoes translational or rotational movements
Solution Approach 1:
The system dynamically adapts the relocalization method based on detected camera motion type. When translational or rotational movement is detected, the system switches from conventional image-to-image or image-to-map methods to a motion-compensated estimation approach, optimizing processing efficiency for each motion scenario
Solution Approach 2:
The system changes the estimation parameters and algorithm selection based on the detected motion parameters. By analyzing camera movement characteristics (translation vs. rotation), the system adjusts the relocalization strategy to minimize computational cost while maintaining accuracy
2Measurement precision
If feature points are used for camera pose estimation, then position and pose can be calculated, but the estimation is temporarily lost when the camera is directed away from the object
Solution Approach 1:
The system performs motion prediction based on previous camera pose and motion trends before feature points become unavailable. This preliminary estimation maintains continuous pose tracking even when the camera moves away from the object and feature points are no longer detectable
Solution Approach 2:
The system uses feedback from detected motion patterns to continuously update the pose estimation. When feature points are lost, the feedback loop switches to using motion-based prediction, and when feature points become available again, it transitions back to feature-based estimation, ensuring continuous and reliable tracking
3Reliability
If relocalization processing is performed when camera position and pose are lost, then estimation can be restarted, but the processing time and computational load increase
Solution Approach 1:
The system dynamically selects the relocalization method based on the type of motion detected. For translational movements, it uses motion-compensated relocalization, while for rotational movements, it employs rotation-aware estimation techniques, reducing the time and computational load compared to conventional relocalization methods
Solution Approach 2:
The system changes relocalization parameters based on motion analysis. By detecting whether the camera underwent translation or rotation, the system adjusts the relocalization algorithm parameters to optimize processing speed and reduce computational requirements while maintaining reliable position recovery
Data Source
AI summary
A method includes determining movement of a camera from a first time point when a first image has been captured to a second time point when a second image has been captured, performing first estimation processing for estimating a position and pose of the camera in the second time point based on image data at the time of capturing, a past image captured in the past, and a past position and pose of the camera at a time point when the past image has been captured, when the movement is not a translational movement and a rotation movement around an optical direction, and performing a second estimation processing for estimating the position and pose based on a feature descriptor of a feature point extracted from the second image and a feature descriptor of a map point accumulated when the movement is the translational movement or the rotational movement.