Method and device for calibrating binocular camera

A method for calibrating a binocular camera, comprising:

• S1 of taking the image set 1 and the image set 2 at the two points separated from each other by a predetermined distance (s),
• S2 of extracting a plurality of feature points from an image set 1 and an image set 2;
• S3 of fitting Gaussian distribution parameters of each feature point, and extracting an expected value as a theoretical disparity;
• S4 of selecting a common feature point, and calculating a first theoretical distance (Z1) and a second theoretical distance (Z2) of the common feature point;
• S5 of obtaining an evaluation index, which comprises:
• S51 of calculating a difference between the first theoretical distance (Z1i) and the second theoretical distance (Z2i) of each selected common feature point according to a formula Δt=Z1i_31 Z2i;
• and S52 of performing the Gaussian fitting on the difference (Δi) between the theoretical distances of each selected common feature point to acquire Gaussian distribution with the expected value (Δc) being equal to the predetermined distance (s), and extracting the variance (σ2) as the evaluation index,
• S6 of determining whether the evaluation index is smaller than a threshold, terminating the calibration when the evaluation index is smaller than the threshold, or proceeding to S7 when the evaluation index is not smaller than the threshold;
• and
• S7 of adjusting posture parameters of the binocular camera, and returning to S3.

A memory device storing therein a plurality of instructions, wherein the instructions are loaded and executed by a processor so as to perform the following steps:

• S2 of extracting feature points from an image set 1 and an image set 2 taken at two points separated from each other by a predetermined distance (s);
• S3 of fitting Gaussian distribution parameters of each feature point, and extracting an expected value as a theoretical disparity;
• S4 of selecting a common feature point, and calculating a first theoretical distance (Z1) and a second theoretical distance (Z2) of the common feature point;
• S5 of performing Gaussian fitting on a difference between the theoretical distances of the common feature point (Δ=Z1−Z2), and extracting a variance (σ2) as an evaluation index;
• S6 of determining whether the evaluation index is smaller than a threshold, if yes, terminating the calibration, and otherwise, proceeding to S7;
• and
• S7 of adjusting posture parameters of the binocular camera, and returning to S3,
• wherein S3 comprises:
• S31 of calculating a disparity of each feature point with respect to each image in the image set 1;
• S32 of performing statistical analysis on the disparities in the predetermined quantity of frames with respect to each feature point to acquire a Gaussian fitting model;
• S33 of selecting the expected value of the Gaussian fitting model as the theoretical disparity of the feature point;
• S34 of repeating S31 to S33 with respect to each feature point in the image set 1 to acquire the theoretical disparities of all the feature points in the image set 1;
• and S35 of repeating S31 to S34 with respect to the image set 2, to acquire theoretical disparities of all the feature points in the image set 2.

A vehicle with a binocular camera, comprising a processor configured to execute a plurality of instructions, and a memory device storing therein the plurality of instructions, wherein the instructions are loaded and executed by the processor so as to perform the following steps:

• S2 of extracting feature points from an image set 1 and an image set 2 taken at two points separated from each other by a predetermined distance (s);
• S3 of fitting Gaussian distribution parameters of each feature point, and extracting an expected value as a theoretical disparity;
• S4 of selecting a common feature point, and calculating a first theoretical distance (Z1) and a second theoretical distance (Z2) of the common feature point;
• S5 of obtaining a variance (σ2) as an evaluation index, which comprises:
• S51 of calculating a difference between the first theoretical distance (Z1i) and the second theoretical distance (Z2i) of each selected common feature point through a formula Δi=Z1i−Z2i;
• and S52 of performing the Gaussian fitting on the difference (Δi) between the theoretical distances of each selected common feature point to acquire Gaussian distribution with the expected value (σc) being equal to the predetermined distance (s), and extracting the variance (σ2) as the evaluation index;
• S6 of determining whether the evaluation index is smaller than a threshold, if yes, terminating the calibration, and otherwise, proceeding to S7;
• and
• S7 of adjusting posture parameters of the binocular camera and returning to S3.