Dual Dome Camera Visual Localization for Stable SLAM Tracking
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Solution Overview
Problem
Existing Monocular VSLAM systems struggle to accurately obtain depth information and suffer from scale drift, while binocular VSLAM systems face issues with feature point extraction due to camera distortion, leading to low positioning accuracy and tracking failures.
Innovation Solution
A method and system utilizing dual dome cameras for visual localization, involving camera calibration, feature point tracking, triangulation-based depth restoration, and bino VSLAM principles to calculate positions and postures, enabling accurate map creation and motion estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If ordinary cameras are used in binocular VSLAM systems, then the device complexity is reduced, but the angle of view is small so that adequate scene feature points cannot be extracted
Solution Approach 1:
The patent employs dome cameras with a hemispherical field of view instead of conventional flat cameras. This spherical/curved optical design captures 180 degrees of the scene, dramatically increasing the quantity of extractable feature points while maintaining relatively simple device architecture. The curved sensor surface naturally maps to the wide angular coverage without requiring complex lens assemblies.
2Quantity of substance
If fisheye cameras are used in binocular VSLAM systems, then the angle of view is increased to extract adequate scene feature points, but large distortion in the images requires distortion correction which causes feature point loss
Solution Approach 1:
The dome camera's curved sensor surface inherently captures images with a different distortion characteristic compared to traditional fisheye lenses. This curved geometry allows for more accurate feature point extraction and tracking while maintaining wide field of view, reducing the need for aggressive distortion correction that would lose feature points.
Solution Approach 2:
The system changes the fundamental optical parameters by using a dome-shaped sensor array rather than a flat sensor with fisheye lens. This parameter change in the camera geometry allows direct capture of panoramic views with reduced distortion, enabling feature point extraction without the trade-off present in traditional fisheye systems.
3Device complexity
If Monocular VSLAM system is used, then the device complexity is reduced, but depth information cannot be directly obtained and scale drift occurs
Solution Approach 1:
The patent merges two dome cameras into a binocular VSLAM system, combining the advantages of wide field of view with stereo depth perception. This merging of multiple cameras provides direct depth information through triangulation while maintaining manageable system complexity through shared processing architecture and synchronized operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances positioning accuracy, stabilizes feature tracking, and directly obtains depth information, suitable for larger scenes and applications like vehicle and robot positioning.
Implementation Method 1
collecting video stream data via the dual dome cameras
Implementation Method 2
restoring depth information of scenes based on a principle of triangulation measurement
Data Source
AI summary
A method and a system for visual localization based on dual dome cameras is based on two synchronized panoramic video streams output by dual dome cameras to solve the problem of fewer feature points and tracking failure, thereby achieving stable visual SLAM tracking. The depth information of the scene is restored via the two panoramic video streams based on the principle of triangulation measurement. The positions and postures of the dual dome cameras are calculated based on a principle of binocular vision based SLAM, so that accurate map information are obtained finally by evaluating the positions and postures of the dual dome cameras corresponding to the key frames and the depth information in the key frames. The disclosure makes up for inaccurate and incomplete depth information of the scenes in passive scene recoveries, which is suitable for vehicle and robot positioning, obstacle detection and free space estimation.