Adaptive Virtual Camera for Sparse SLAM Pose Estimation
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Solution Overview
Problem
Current sparse indirect SLAM methods face challenges in accurate pose estimation due to high geometric errors for objects/landmarks farther from the camera, which impacts the optimization process and fidelity of pose estimation.
Innovation Solution
The implementation of an adaptive virtual camera system that adjusts its pose based on the estimated physical camera pose, feature point position, and landmark position, projecting 3D errors onto an image plane to maintain depth error components and improve error terms in all dimensions, thereby enhancing pose estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D geometric error is re-projected back to the image plane to normalize error representation, then pose estimation fidelity is improved by removing biases towards farther objects, but the depth component of geometric error is lost causing negative impact on pose estimation
Solution Approach 1:
The patent introduces a virtual camera positioned at a different depth location than the physical camera. By projecting 3D geometric errors onto the virtual camera's image plane, the system maintains depth information that would otherwise be lost in traditional 2D re-projection. This dimensional transformation allows errors from objects at different depths to be preserved and utilized in pose optimization.
Solution Approach 2:
The virtual camera acts as an intermediary between the physical camera and the pose optimization process. It serves as a mediator that transforms 3D geometric errors into a form that preserves depth information while being suitable for optimization, thereby resolving the conflict between normalization and depth preservation.
2Measurement precision
If depth sensors are used for 3D feature estimation, then geometric error is reduced for closer objects, but accuracy decreases for farther objects due to inverse relationship between depth accuracy and distance
Solution Approach 1:
The patent applies different error weighting strategies based on local conditions. By using a virtual camera at a specific depth position, the system optimizes error representation for different depth ranges. This allows closer objects to maintain their naturally lower errors while distant objects benefit from the transformed error metric that compensates for depth sensor limitations.
Solution Approach 2:
The system changes the parameter space by introducing a virtual camera position parameter. This transformation modifies how depth errors are represented and weighted in the optimization process, effectively adjusting the error metric to account for the inverse relationship between depth accuracy and distance.
3Productivity
If traditional re-projection error is used in optimization, then computational efficiency is maintained, but pose estimation accuracy deteriorates due to squashing of depth component
Solution Approach 1:
The patent creates a virtual copy of the camera system with modified positioning. This virtual camera copy processes the same image data but transforms the error representation in a way that preserves depth information. The copying approach maintains computational efficiency while improving accuracy by avoiding direct modification of the physical camera system.
Data Source
AI summary
Techniques related to indirect sparse simultaneous localization and mapping (SLAM) are discussed. Such techniques include adaptively positioning a virtual camera relative to an estimated position of a physical camera within an environment to be mapped, projecting a depth error to an image plane corresponding to the adaptive camera position, and using the projected depth error to update a mapping of the environment.