Flexible Camera Rig SLAM Accuracy via Deformation Compensation
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Solution Overview
Problem
Wearable machine-vision systems with non-rigid camera rigs face challenges in maintaining accurate simultaneous location and mapping (SLAM) due to uncertainties caused by physical deformation, such as twisting and bending, which affect the alignment and orientation of cameras, leading to unreliable depth-sensing errors.
Innovation Solution
Computing parameter values based on image data to account for physical deformation of the camera rig, allowing for dynamic adjustment of camera positions and orientations, which determines the relative fields of view and improves the accuracy of SLAM by modeling deformations such as twists and bends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a rigid camera rig is used, then SLAM accuracy is maintained, but the system becomes impractical for wearable applications due to weight and comfort constraints
Solution Approach 1:
The patent transitions from a static rigid rig model to a dynamic flexible rig model that can adapt to physical deformations. The system continuously estimates rig deformation parameters and updates camera pose estimates in real-time, allowing the lightweight flexible rig to maintain SLAM accuracy despite changing physical conditions during wearability.
Solution Approach 2:
The patent introduces deformation parameters (such as bend angles, twist angles, and rig transformation matrices) that dynamically change with the physical state of the flexible rig. By monitoring and compensating for these parameter changes, the system maintains accurate camera positioning and orientation even as the rig deforms under different wearing conditions.
2Ease of operation
If a flexible camera rig is used, then weight and comfort are improved, but SLAM accuracy deteriorates due to physical deformation uncertainties
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors image data from multiple cameras, estimates rig deformation based on observed disparities, and uses this information to correct camera pose estimates. This closed-loop approach compensates for deformation-induced errors in real-time, maintaining depth-sensing accuracy despite the flexibility of the rig.
Solution Approach 2:
The patent replaces the mechanical rigid structure with a flexible structure and substitutes the mechanical stability with computational compensation. Instead of relying on the rig's physical rigidity to maintain camera alignment, the system uses algorithms to calculate and correct for misalignments caused by deformation, achieving accuracy through software rather than hardware rigidity.
3Device complexity
If physical deformation is not compensated, then system complexity is reduced, but SLAM reliability deteriorates due to alignment and orientation uncertainties
Solution Approach 1:
The patent creates a universal deformation compensation framework that handles multiple types of rig deformations (bending, twisting, stretching) using a unified mathematical model. The same estimation and compensation algorithms apply regardless of the specific deformation type, making the system robust across various wearing conditions without requiring separate mechanisms for each deformation mode.
Data Source
AI summary
Embodiments related to mapping an environment of a machine-vision system are disclosed. For example, one disclosed method includes acquiring image data resolving one or more reference features of an environment and computing a parameter value based on the image data, wherein the parameter value is responsive to physical deformation of the machine-vision system.