Research on Binocular Ranging System Based on Image Features

doi:10.16182/j.issn1004731x.joss.20-0793

Abstract

Abstract:

Aiming at the problems of large measurement error, single image information, and poor real-time performance in binocular vision ranging, a binocular ranging method based on ORB (oriented fast and rotated brief) features is proposed. Median filtering is performed on the video frame, the ORB feature of the image is extracted, and the Hamming distance with the best matching effect is selected through experiments. The RANSAC (random sample consensus) model estimation is performed on the selected matching points, the mismatches are removed, the model relationship between parallax and true distance is analyzed, the optimal ranging model is constructed and verified on the experimental platform. The results show that the proposed method has the advantages of accurate ranging, fast running speed and strong robustness compared with other binocular ranging methods, and can display the distance information of the features in the image in real time.

Key words: oriented fast and rotated brief（ORB）, feature matching, random sample consensus（RANSAC）, model optimization, binocular ranging

CLC Number:

TP391.41

Jinghui Yang, Dekang Liu, Wanhe Du, Lining Xing. Research on Binocular Ranging System Based on Image Features[J]. Journal of System Simulation, 2022, 34(3): 624-632.

Figures/Tables 17

Fig. 1

Fig. 2

Fig. 3

Table 1

Left and right camera internal parameters

相机	$f$ (pixel)	$k 1$	$k 2$	$k 3$	$P 1$	$P 2$
左	832.320	-0.083 1	0.278 6	-0.258 3	-0.002 8	0.002 2
右	832.320	-0.054 7	0.128 9	-0.086 3	0	0.001 4

Table 1

Fig. 4

Fig. 5

Fig. 6

Table 2

Fig. 7

Table 3

Fig. 8

Fig. 9

Table 4

Comparison of R2, SSE and RMSE of three fitting methods

拟合方式	$R 2$	SSE	RMSE
高斯拟合	0.999 3	4 166	14.08
多项式拟合	0.999 6	1 975	9.071
幂函数拟合	0.999 9	617	4.694

Table 4

Fig. 10

Fig. 11

Fig. 12

Fig. 13

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汉明距离	总点数	筛选后点数	RANSAC剔除
20	1 000	141	131
25	1 000	221	195
30	1 000	331	294
35	1 000	422	364
40	1 000	501	413
45	1 000	569	414

z/mm	d(pixel)	z/mm	d(pixel)	z/mm	d(pixel)
300	355.368	800	128.099	1 300	77.895
350	300.087	850	119.862	1 350	74.850
400	259.303	900	113.026	1 400	71.942
450	230.157	950	107.113	1 450	69.765
500	206.663	1 000	101.946	1 500	67.679
550	186.678	1 050	97.022	1 550	64.846
600	171.513	1 100	92.237	1 600	63.482
650	157.999	1 150	88.013	1 650	61.070
700	146.544	1 200	84.834	1 700	59.866
750	135.873	1 250	80.920	1 750	57.554

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