Pose Estimation Characterization Using Fiducial Scene Transformation
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Solution Overview
Problem
Existing pose estimation systems face challenges in accurately characterizing rotational and translational errors at high precision due to the lack of effective methods for obtaining ground truth poses, especially for small and transparent objects, and the complexity of transforming between coordinate spaces.
Innovation Solution
A method and system that utilizes a fiducial marker and a support platform to rigidly transform an object arrangement, enabling the computation of coarse and refined scene transformations, and matching poses to determine average rotation and translation errors using 3-D models and cost functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pose estimation methods are used, then the system can estimate object positions and orientations, but the measurement precision is limited to 300 microns or more
Solution Approach 1:
The patent introduces a fiducial marker as an intermediary object with known geometry and high-precision detectable features. This fiducial serves as a mediator between the measurement system and the objects being measured, enabling accurate computation of transformation matrices. The fiducial's known pose provides a reliable reference frame that allows the system to achieve sub-300-micron precision by comparing measured fiducial poses against their ground truth positions.
Solution Approach 2:
The patent creates a digital copy or model of the physical scene by computing transformation matrices that map camera coordinates to world coordinates. This digital representation (transformation matrix) allows for precise mathematical manipulation and error characterization without physically moving the measurement system, enabling repeated high-precision measurements through computational transformation.
2Measurement precision
If the system attempts to measure small and transparent objects, then object detection capability is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The fiducial marker acts as an intermediary that is easily detectable and provides a stable reference frame. By attaching or positioning the fiducial with the difficult-to-detect object, the system can indirectly measure the object's pose through the fiducial's known relationship to the object, bypassing the detection difficulties of the transparent or small object itself.
Solution Approach 2:
The patent changes the measurement parameters by using a fiducial marker with high-contrast or easily detectable features instead of directly measuring the difficult objects. This parameter change (from direct object measurement to fiducial-based indirect measurement) transforms an intractable measurement problem into a solvable one while maintaining the ability to characterize the original objects' poses.
3Measurement precision
If coordinate space transformation is performed to compute pose errors, then error characterization is enabled, but the device complexity increases
Solution Approach 1:
The patent extracts the essential transformation information by focusing on the fiducial marker's pose and its relationship to the camera coordinate system. By taking out only the necessary transformation parameters (rotation and translation components) from the full coordinate transformation problem, the system simplifies the computational complexity while maintaining the ability to compute accurate pose errors through targeted matrix operations.
4Measurement precision
If a fiducial marker is introduced to improve ground truth accuracy, then measurement precision is improved, but the device complexity increases
Solution Approach 1:
The fiducial marker serves as a simple intermediary that adds minimal complexity while providing substantial measurement precision improvements. The fiducial's known geometry and easily detectable features create a reliable reference that simplifies the overall measurement process by providing explicit ground truth positions, thereby reducing the computational burden of pose estimation while enhancing accuracy.
Data Source
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AI summary
A method for characterizing a pose estimation system includes: receiving, from a pose estimation system, first poses of an arrangement of objects in a first scene: receiving, from the pose estimation system, second poses of the arrangement of objects in a second scene, the second scene being a rigid transformation of the arrangement of objects of the first scene with respect to the pose estimation system; computing a coarse scene transformation between the first scene and the second scene; matching corresponding poses between the first poses and the second poses; computing a refined scene transformation between the first scene and the\ second scene based on coarse scene transformation, the first poses, and the second poses; transforming and computing an average rotation error and an average translation error of the pose estimation system.