SLAM Feature Tracking via Homography Matrices for Strong Rotation
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Solution Overview
Problem
Traditional SLAM methods face challenges in handling strong rotation and fast motion, leading to tracking failures and map extension delays, especially when feature matching and depth estimation struggle with large parallax and motion blur.
Innovation Solution
A method that processes video streams in real-time, using global and local homography matrices for feature tracking, quality evaluation, and adaptive map optimization, including foreground thread processing for local map extension and background thread optimization, to handle strong rotation and fast motion effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SLAM methods use feature matching for camera tracking, then mapping precision can be maintained, but tracking reliability deteriorates under strong rotation and fast motion conditions
Solution Approach 1:
The patent segments the tracking process into multiple quality levels (good, medium, poor tracking quality) and applies different strategies for each. For poor quality tracking under strong rotation, the system triggers relocation using previously established keyframes, while for good quality tracking, normal feature matching continues. This segmentation allows the system to maintain reliability by switching strategies based on motion conditions while preserving mapping precision when conditions permit.
Solution Approach 2:
The patent implements dynamic adjustment of tracking strategies based on real-time motion detection. The system monitors tracking quality metrics and dynamically switches between normal operation mode and relocation mode. This dynamic adaptation allows the system to respond to changing motion conditions, maintaining tracking reliability during fast motion while preserving mapping precision during stable conditions.
2Measurement precision
If key frame based SLAM performs bundle adjustment for high precision, then mapping precision improves, but processing time increases causing map extension delays
Solution Approach 1:
The patent applies partial bundle adjustment only when necessary - specifically when tracking quality deteriorates to poor levels or when relocation is triggered. During normal operation with good tracking quality, the system continues with lighter processing, performing only essential updates. This partial application of computationally intensive operations reduces overall processing time and map extension delays while maintaining mapping precision when needed.
Solution Approach 2:
The patent applies different processing intensities to different regions of the tracking pipeline. For frames with good tracking quality, minimal processing is applied. For frames with poor tracking quality or during relocation events, full bundle adjustment is performed on the affected local region. This local quality approach ensures mapping precision is maintained in critical areas while reducing overall computational burden and time loss.
3Reliability
If SLAM system increases feature matching frequency to handle fast motion, then tracking reliability improves, but computational complexity increases
Solution Approach 1:
The patent dynamically adjusts the intensity of feature matching operations based on detected motion conditions. During periods of strong rotation or fast motion, the system increases matching frequency and triggers relocation events more frequently. During stable periods, normal operation continues with standard processing frequency. This dynamic adjustment maintains tracking reliability during challenging conditions while avoiding unnecessary computational complexity during stable conditions.
4Measurement precision
If traditional SLAM uses sufficient parallax between key frames to constrain three-dimensional points, then mapping precision improves, but adaptability deteriorates under strong rotation where parallax cannot be satisfied
Solution Approach 1:
The patent inverts the traditional approach by using previously established keyframes to relocate the camera during strong rotation, rather than relying on forward parallax between sequentially captured keyframes. When strong rotation prevents sufficient parallax accumulation, the system reverses the dependency direction by matching current frame features against historical keyframes, enabling precise localization even when conventional parallax constraints cannot be satisfied.
Data Source
AI summary
A method for simultaneous localization and mapping is provided, which can reliably handle strong rotation and fast motion. The method provided a simultaneous localization and mapping algorithm framework based on a key frame, which can support rapid local map extension. Under this framework, a new feature tracking method based on multiple homography matrices is provided, and this method is efficient and robust under strong rotation and fast motion. A camera orientation optimization framework based on a sliding window is further provided to increase motion constraint between successive frames with simulated or actual IMU data. Finally, a method for obtaining a real scale of a specific plane and scene is provided in such a manner that a virtual object is placed on a specific plane in real size.