基于阈值自适应调整的图像特征均匀分布ORB算法改进

doi:10.3969/j.issn.1674-8484.2021.03.004

汽车安全与节能学报 ›› 2021, Vol. 12 ›› Issue (3): 305-313.DOI: 10.3969/j.issn.1674-8484.2021.03.004

基于阈值自适应调整的图像特征均匀分布ORB算法改进

时培成¹(), 杨剑锋¹, 梁涛年², 齐恒¹

1.安徽工程大学汽车新技术安徽省工程技术研究中心,芜湖 241000,中国
2.芜湖伯特利汽车安全系统股份有限公司,芜湖 241009,中国

收稿日期:2021-04-11 出版日期:2021-09-30 发布日期:2021-10-09
作者简介:时培成（1976—）,男（汉族）,安徽,教授。E-mail: shipeicheng@126.com。
基金资助:
国家自然科学基金(51575001);安徽高校科研平台创新团队建设项目(2016-2018);安徽省发改委支持研发创新类项目([2020]479)

Advanced ORB algorithm of image feature uniform distribution based on threshold adaptive

SHI Peicheng¹(), YANG Jianfeng¹, LIANG Taonian², QI Heng¹

1. Anhui Polytechnic University, Automotive New Technology Anhui Engineering and Technology Research Center, Wuhu 241000, China
2. Wuhu Bethel Automotive Safety Systems Co., Ltd, Wuhu 241009, China

Received:2021-04-11 Online:2021-09-30 Published:2021-10-09

摘要/Abstract

摘要：

基于相机的无人驾驶汽车视觉同步定位与地图构建（SLAM）,可完成无人驾驶汽车的定位与建图。针对传统ORB（Oriented FAST and Rotated BRIEF）算法在提取图像特征点时容易造成冗杂、分布集中的问题,提出一种限制四叉树算法分裂深度的改进ORB (A-ORB)算法。该算法构造图像金字塔解决尺度不变性问题;根据所提取的特征点总数计算出每层金字塔所需要提取的特征点数;对每层金字塔图像采用自适应区域划分,根据图像信息计算特征点提取阈值;利用改进四叉树算法来均匀化分布特征点。进行了模拟实验。结果表明:相较于ORB、MA以及S-ORB算法,该算法运行效率提高了30%以上,匹配精度提高了10%以上。

关键词: 无人驾驶汽车, 同步定位与地图构建(SLAM), 运动信息, ORB算法, 图像特征点提取, 图像金字塔, 均匀分布

Abstract:

Camera-based visual SLAM (simultaneous localization and mapping) for driverless vehicles could be used to complete the localization and mapping of driverless vehicles. In order to solve the problems that the traditional ORB (Oriented FAST and Rotated BRIEF) algorithm could easily cause clutter and concentration of distribution when extracting image feature points, an advanced ORB (A-ORB) algorithm was proposed which limited the splitting depth of quadtree algorithm. This algorithm constructed an image pyramid to solve the scale invariance problem. According to the total number of feature points extracted, the algorithm calculated the feature points that needed to be extracted for each layer of the pyramid. The image of each pyramid layer was divided into adaptive regions and the threshold of feature point extraction was calculated according to the image information. This algorithm used improved quadtree algorithm to homogenize the distribution feature points. The simulation experiment was carried out. The results show that compared with ORB, MA and S-ORB algorithms, the running efficiency of the advanced ORB is increased by more than 30%, and the matching accuracy is increased by more than 10%.

Key words: driverless cars, simultaneous localization and mapping (SLAM), movement information, ORB (Oriented FAST and Rotated BRIEF) algorithm, feature point extraction, image pyramid, uniform distribution

中图分类号:

TP391.4

时培成, 杨剑锋, 梁涛年, 齐恒. 基于阈值自适应调整的图像特征均匀分布ORB算法改进[J]. 汽车安全与节能学报, 2021, 12(3): 305-313.

SHI Peicheng, YANG Jianfeng, LIANG Taonian, QI Heng. Advanced ORB algorithm of image feature uniform distribution based on threshold adaptive[J]. Journal of Automotive Safety and Energy, 2021, 12(3): 305-313.

图/表 9

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图像数据集	均匀度,u				运行时间,t / ms
图像数据集	算法ORB	MA	S-ORB	A-ORB	算法ORB	MA	S-ORB	A-ORB
Bark	198.65	156.23	212.34	160.58	20.45	23.06	9.67	10.73
Bikes	201.54	162.35	205.62	170.64	22.36	26.36	10.35	12.56
Boat	196.25	140.35	200.36	157.23	19.56	25.41	9.96	13.56
Leuven	192.33	153.24	198.76	155.33	21.47	27.68	10.36	11.39
Ubc	168.56	126.58	189.62	155.67	18.36	24.59	9.37	10.95
Graf	202.36	145.69	210.56	165.69	20.67	27.53	10.69	14.72
平均值	193.28	147.40	202.86	160.86	20.47	25.77	10.06	12.32

图像数据集	均匀度,u				运行时间,t / ms
图像数据集	算法ORB	MA	S-ORB	A-ORB	算法ORB	MA	S-ORB	A-ORB
Bark	198.65	156.23	212.34	160.58	20.45	23.06	9.67	10.73
Bikes	201.54	162.35	205.62	170.64	22.36	26.36	10.35	12.56
Boat	196.25	140.35	200.36	157.23	19.56	25.41	9.96	13.56
Leuven	192.33	153.24	198.76	155.33	21.47	27.68	10.36	11.39
Ubc	168.56	126.58	189.62	155.67	18.36	24.59	9.37	10.95
Graf	202.36	145.69	210.56	165.69	20.67	27.53	10.69	14.72
平均值	193.28	147.40	202.86	160.86	20.47	25.77	10.06	12.32

图像数据集	算法	总匹配数	正确匹配数	正确率 / %	t / ms
Leuven	ORB	500	256	51.2	1.36
	MA	500	354	70.8	1.43
	S-ORB	506	324	64.0	1.23
	A-ORB	502	418	83.3	1.31
Bikes	ORB	500	282	56.4	1.41
	MA	500	363	72.6	1.53
	S-ORB	502	304	60.7	1.32
	A-ORB	504	413	81.9	1.33
Ubc	ORB	500	412	82.4	1.37
	MA	500	442	88.4	1.44
	S-ORB	506	398	78.7	1.23
	A-ORB	507	478	94.3	1.28
Bark	ORB	500	221	44.2	1.43
	MA	500	246	49.2	1.53
	S-ORB	508	197	38.8	1.24
	A-ORB	504	300	59.5	1.35
Boat	ORB	500	232	46.4	1.43
	MA	500	287	57.4	1.55
	S-ORB	502	267	53.2	1.29
	A-ORB	510	313	61.4	1.41
Graf	ORB	500	241	48.2	1.41
	MA	500	286	57.2	1.53
	S-ORB	509	239	47.0	1.31
	A-ORB	505	345	68.3	1.36

图像数据集	算法	总匹配数	正确匹配数	正确率 / %	t / ms
Leuven	ORB	500	256	51.2	1.36
	MA	500	354	70.8	1.43
	S-ORB	506	324	64.0	1.23
	A-ORB	502	418	83.3	1.31
Bikes	ORB	500	282	56.4	1.41
	MA	500	363	72.6	1.53
	S-ORB	502	304	60.7	1.32
	A-ORB	504	413	81.9	1.33
Ubc	ORB	500	412	82.4	1.37
	MA	500	442	88.4	1.44
	S-ORB	506	398	78.7	1.23
	A-ORB	507	478	94.3	1.28
Bark	ORB	500	221	44.2	1.43
	MA	500	246	49.2	1.53
	S-ORB	508	197	38.8	1.24
	A-ORB	504	300	59.5	1.35
Boat	ORB	500	232	46.4	1.43
	MA	500	287	57.4	1.55
	S-ORB	502	267	53.2	1.29
	A-ORB	510	313	61.4	1.41
Graf	ORB	500	241	48.2	1.41
	MA	500	286	57.2	1.53
	S-ORB	509	239	47.0	1.31
	A-ORB	505	345	68.3	1.36

基于阈值自适应调整的图像特征均匀分布ORB算法改进

Advanced ORB algorithm of image feature uniform distribution based on threshold adaptive

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