Dual-Algorithm Pose Tracking for Rapid Camera Motion
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Solution Overview
Problem
Existing real-time camera tracking methods lack robustness and are sensitive to input data quality and user photography methods, making them unsuitable for ordinary users, especially in situations where the scanned object deviates from the field of vision or the camera moves rapidly.
Innovation Solution
A pose tracking method that uses a combination of algorithms for pixel-by-pixel color alignment and iterative point cloud alignment, along with an inertial navigation module for state estimation, to accurately update the camera pose and maintain tracking even when the object moves out of the field of vision or the camera experiences rapid motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single algorithm is used for real-time camera tracking, then the processing speed is maintained, but the robustness deteriorates and tracking loss occurs when the object deviates from the field of vision or camera moves rapidly
Solution Approach 1:
The tracking system is segmented into two independent algorithms: a first algorithm for real-time pose estimation and a second algorithm for verification. Each algorithm operates independently with its own processing pipeline, allowing the system to maintain real-time performance while improving robustness through diversified processing approaches.
Solution Approach 2:
The system implements feedback by using the second algorithm to verify the results produced by the first algorithm. When verification succeeds, the pose information is confirmed; when it fails, the system triggers relocation operations. This feedback mechanism ensures tracking robustness without sacrificing real-time performance.
2Measurement precision
If high-quality input data and strict photographing methods are required, then the tracking accuracy is improved, but the ease of operation deteriorates making it unsuitable for ordinary users
Solution Approach 1:
The system performs preliminary actions by continuously capturing multiple frame images and pre-processing them through the first algorithm before verification is needed. This preparation work is done in advance so that when verification or relocation is required, the system can respond quickly without requiring users to adjust their photographing behavior.
Solution Approach 2:
The dual-algorithm system provides self-service by automatically detecting tracking quality and triggering relocation operations when needed. The system monitors its own tracking state through verification and autonomously corrects issues without user intervention, maintaining high accuracy while requiring minimal user skill.
3Adaptability or versatility
If the camera moves rapidly or the object deviates from the field of vision, then the adaptability to dynamic scenarios is improved, but the tracking stability deteriorates causing frequent tracking loss
Solution Approach 1:
The system dynamically adjusts its operation mode based on real-time verification results. During normal tracking, it operates in standard mode for efficiency. When rapid motion or object deviation is detected, it automatically triggers relocation operations. This dynamic adaptation allows the system to handle diverse scenarios while maintaining overall tracking stability.
Solution Approach 2:
The system prepares for potential tracking failures by having the second algorithm ready for verification and relocation methods pre-configured. This beforehand cushioning ensures that when rapid motion or deviation occurs, the system can quickly transition to corrective actions without losing tracking stability.
Data Source
AI summary
A pose tracking method, a pose tracking device and an electronic device. The method comprises: acquiring continuous multiple images of a scanned object and an initial pose of an image capturing unit (S10); by taking the initial pose as an initial value, acquiring, on the basis of a previous frame image and a current frame image in the continuous multiple-frame images, a first calculated pose of the current frame image by using a first algorithm (S12); by taking the first calculated pose as an initial value, acquiring, on the basis of the current frame image and a current frame image reconstruction model, a second calculated pose of the current frame image by using a second algorithm (S14); and updating the initial pose of the image capturing unit according to the second calculated pose, and repeating the described steps to achieve pose tracking of the image capturing unit (S16).