Pose Estimation Using Motion and Vision Sensor Fusion
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Solution Overview
Problem
Current pose estimation methods for cameras in dynamic environments face challenges in accurately determining translation and rotation information, particularly in fields like SLAM, mixed reality, and 3D scene reconstruction, due to limitations in sensor reliability and optimization techniques.
Innovation Solution
A pose estimation method that utilizes a combination of motion sensors and vision sensors to determine rotation and translation components, where the rotation component is maintained constant while the translation component is optimized based on change amounts, and additional optimization is requested only when necessary, using a sensor fusion scheme to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion sensor output is used directly as rotation component and translation component is optimized, then pose estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The pose estimation problem is segmented into two independent components: rotation estimation using motion sensor output and translation estimation using vision sensor optimization. This segmentation allows each component to be processed separately with appropriate methods, improving overall accuracy while managing computational complexity through division of labor between sensor types.
Solution Approach 2:
The rotation component is determined preliminarily from motion sensor output before translating optimization to the translation component. This preliminary action establishes a fixed reference frame that simplifies subsequent translation optimization, reducing the search space and computational burden of the optimization process.
2Measurement precision
If translation component is optimized iteratively, then pose estimation accuracy is improved, but processing time increases
Solution Approach 1:
The rotation component is fixed preliminarily from motion sensors before translation optimization begins. This preliminary action eliminates rotation as a variable in the optimization process, significantly reducing the dimensionality of the optimization problem and the number of iterations required to converge to an accurate solution.
Solution Approach 2:
The optimization process uses feedback from image matching errors to iteratively adjust the translation component. The feedback mechanism allows the system to converge to an accurate solution efficiently by learning from each iteration's performance, balancing accuracy improvement with processing time through adaptive stopping criteria.
3Reliability
If sensor fusion scheme is applied, then pose estimation reliability is improved, but system complexity increases
Solution Approach 1:
The sensor fusion system is segmented into distinct functional modules: motion sensor processing for rotation, vision sensor processing for translation, and an integration layer that combines results. This modular segmentation improves reliability through cross-validation of sensor data while managing system complexity through clear separation of concerns and standardized interfaces between modules.
Solution Approach 2:
The system merges data from motion sensors and vision sensors into a unified pose estimation framework. By combining the complementary strengths of inertial measurement (high temporal resolution for rotation) and visual measurement (spatial context for translation), the system achieves enhanced reliability that exceeds what either sensor type could provide alone.
Data Source
AI summary
A pose estimation method and apparatus are provided. The method includes determining, as a rotation component, an output value of a motion sensor configured to sense a motion of the pose estimation apparatus, determining a change amount of a translation component based on the rotation component, and the translation component extracted from images photographed by a vision sensor included in the pose estimation apparatus, optimizing the translation component based on the change amount, and outputting the rotation component and the optimized translation components a pose estimation value of the pose estimation apparatus.