Method and system for evaluating relative precision confidence coefficient of vehicle-end monocular vision measurement
A technology of monocular vision and relative precision, which is applied in measuring devices, optical devices, image analysis, etc., can solve problems such as vehicle relative pose errors, IMU sensor process errors, etc., to improve accuracy and reliability, and improve data fusion Accuracy, the effect of improving fusion accuracy
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0046] Embodiment 1 provided by the present invention is an embodiment of a method for evaluating the confidence degree of relative accuracy of monocular vision measurement at the vehicle end provided by the present invention, such as figure 2 Shown is the flow chart of the relative accuracy error propagation of the monocular vision measurement at the vehicle end provided by the embodiment of the present invention, combined with figure 1 and figure 2 As can be seen, this embodiment includes:
[0047] Step 1. Initialize the covariance matrix of the monocular vision system according to the prior knowledge. The covariance matrix includes: the pose covariance C(X k-1 ) and the relative pose covariance C(X (k-1)k ) and feature point matching pixel error covariance matrix C between two frames of images p .
[0048] Step 2, according to the covariance matrix C p and the pose covariance C(X k ) to obtain the position covariance C of the target for position estimation under rel...
Embodiment 2
[0067] Embodiment 2 provided by the present invention is an embodiment of a vehicle end monocular vision measurement relative accuracy confidence evaluation system provided by the present invention, such as Figure 4 Shown is a structural block diagram of an embodiment of a vehicle-end monocular vision measurement relative accuracy confidence evaluation system provided by the present invention, consisting of Figure 4 It can be seen that the system includes: an initialization module 101 , a target location covariance determination module 102 and a confidence degree determination module 103 .
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 


