Panoramic Look-Around View Fusion for Underbody Blind Areas
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Solution Overview
Problem
Existing panoramic look-around systems for vehicles cannot capture images of the underbody region, resulting in a blind area that affects the analysis of road conditions and position judgment during driving.
Innovation Solution
A method that acquires images from multiple cameras, uses the ORB algorithm to extract characteristic points, and calculates an affine transformation matrix based on steering wheel angle and vehicle speed information to integrate previous and current views, effectively eliminating the blind area by generating continuously updated panoramic look-around views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional cameras are installed on the vehicle to capture surrounding environment, then the panoramic look-around view can be generated, but the underbody region cannot be captured forming a blind area
Solution Approach 1:
The system performs preliminary actions by capturing images from multiple cameras at previous time moments and storing them. These pre-captured images are then used to fill in the underbody blind areas in the current panoramic view through affine transformation and fusion, eliminating the need for additional specialized underbody cameras.
Solution Approach 2:
The system creates a virtual copy of the underbody region by transforming and fusing images from existing cameras that captured the underbody at different angles and times. This virtual copy is then integrated into the panoramic view to fill the blind area, avoiding the need for physical underbody-mounted cameras.
2Measurement precision
If characteristic point matching is performed between previous and current look-around views, then the calculation amount is extremely large reducing real-time processing capability
Solution Approach 1:
The matching process is segmented into two stages: first matching a small number of keypoint pairs to determine the affine transformation matrix, then using this matrix to guide the fusion of entire images. This segmentation reduces the computational burden from matching all characteristic points to matching only essential keypoints.
Solution Approach 2:
The system performs preliminary action by pre-calculating the affine transformation matrix from a small subset of matched keypoints. This matrix is then used to guide the subsequent image fusion process, avoiding the need to perform exhaustive characteristic point matching on the entire images, thus enabling real-time processing.
Data Source
AI summary
The current disclosure relates to a panoramic look-around view generation method, an in-vehicle device and an in-vehicle system. The method comprises the following steps of: acquiring images of areas around a vehicle, steering wheel angle information and vehicle speed information; transforming and mosaicking the images to generate a time-related look-around view, and using an ORB algorithm to extract characteristic points; using steering wheel angle information and vehicle speed information to calculate a positron of a characteristic point of the look-around view at previous time in the look-around view at current time according to a vehicle motion model, and selecting a characteristic point located near the position in the look-around view at current time to be matched with the characteristic point of the look-around view at previous time; calculating an affine transformation matrix and performing affine transformation, and performing weighted fusion with the look-around view at current time and storing it; repeating the above steps to obtain a continuously updated panoramic look-around view. The method, the device and the system can eliminate blind area in the underbody region, realize a panoramic perspective view, and have fast calculation and high accuracy.